Physical Sciences Forum doi: 10.3390/psf2026013013

Authors: Candelaria Migliaro Juan Martín Randazzo Renata Della Picca

We present a theoretical description of atomic strong-field photoionization. Specifically, we consider an atom driven by a combination of two electromagnetic fields: a high-frequency field assisted by an intense laser of lower frequency. We investigate the photoelectron spectrum (PES) as the sum of two contributions: direct ionization due to the laser field and the photoionization term associated with the high-frequency field. We identify the contributions of above-threshold ionization (ATI) and laser-assisted photoemission (LAPE) structures in the total spectra, even when they overlap. As a particular case, we investigate the situation where an ATI peak coincides with a sideband. Our theoretical scheme for hydrogen initially in the 1 s quantum state and based on strong field approximation is general enough to be applied to other atomic species and field configurations.

Physical Sciences Forum doi: 10.3390/psf2026014009

Authors: Masoumeh Moazzen Sorkhi Zahra Ghalenovi

In this work, we investigate the localization properties of five-dimensional Elko spinor fields on the Bloch brane, a thick brane generated by two interacting scalar fields and characterized by a rich internal structure. We begin by studying the dynamics of an Elko field in the five-dimensional Bloch brane background and derive the corresponding Schrödinger-like equation governing the Kaluza–Klein modes. Our analysis shows that, due to the specific geometric features and the non-trivial warp factor of the Bloch brane, the zero mode of a free Elko field is not normalizable and therefore cannot be localized on the brane. To resolve this issue, we introduce a non-minimal coupling term into the five-dimensional Elko spinor field action. By considering a different form of coupling between the Elko field and the background scalar fields, we demonstrate that the localization of the Elko zero mode can be achieved. We identify the explicit conditions under which a normalizable and physically acceptable zero mode can be trapped on the brane. Our results reveal that the brane’s internal layers play a crucial role in shaping the effective potential felt by the Elko field, thus affecting both the localization behavior and the profile of the zero mode.

Physical Sciences Forum doi: 10.3390/psf2026014008

Authors: Paola Marziani Benedetta Dalla Barba Luigi Foschini

The quasar Eigenvector-1/Main Sequence (E1/MS) provides a physically motivated empirical framework to organize the spectroscopic diversity of type 1 active galactic nuclei (AGN). In its optical plane, the full width at half maximum of H β and the Fe II strength ratio R FeII define a sequence that is primarily driven by Eddington ratio, with important secondary roles played by black-hole mass, orientation, spectral energy distribution, and chemical enrichment. The E1/MS framework is therefore well suited to identifying highly accreting and possibly super-Eddington (SE) sources, usually associated with the extreme Population A (xA) spectral types. In this study, we discuss why E1/MS is a useful tool to search for SE accretors among jetted AGN and, conversely, to place γ -ray-detected AGN in the broader context of quasar phenomenology. We summarize two complementary results: (1) some candidate SE accretors show radio properties such as high brightness temperature non-thermal cores or radio lobes consistent with jet activity and (2) a subset of low-redshift γ -ray narrow-line Seyfert 1 galaxies exhibit optical spectra consistent with xA or borderline-xA classification. We also expand the discussion of recent developments in E1/MS studies, including metallicity trends, the spectral energy distribution of xA quasars, and the role of highly accreting quasars as discovery tools for extreme accretion states, as probes of quasars at the reionization epoch, and as possible cosmological probes.

Physical Sciences Forum doi: 10.3390/psf2026013012

Authors: Sultana N. Nahar

Artificial intelligence (AI) has become an integral part of our daily lives as a most extraordinary assistant for providing information and carrying out various tasks. We can have an extensive amount of work done by a dedicated AI that we cannot do ourselves due to our easily distracted minds. AI is given vast amounts of information that we have been gathering for a long time. Using that information, it can analyze large amounts of data and sort it into particular topics and narrow down the solutions or choices we are interested in. With highly sophisticated high-resolution ground- and space-based telescopes and observatories, we have been gathering huge amounts of data. The analysis of these data will require both a considerable amount of manpower and time. Both of these factors can be reduced easily if we train dedicated AIs for spectral line identification and plasma modeling for analysis. While high-accuracy atomic data are needed for precise astrophysical plasma modeling, there are many gaps in the data due to computations of these high-accuracy data being complex and numerically challenging and requiring a significant amount of time. These problems can also be tackled to a very good approximation by training AI for simulations of existing high-accuracy data and producing data that are missing. A general algorithm for such predicted data and its implementation in astrophysical applications is presented. A recent application of AI in measuring oxygen abundance in galaxies is also presented.

Physical Sciences Forum doi: 10.3390/psf2026015002

Authors: Meng Wang Yumin Liu

Metalenses based on optical metasurfaces enable wavefront manipulation using subwavelength nanostructures and provide a promising route toward compact and integrated optical systems. However, strong chromatic aberration caused by wavelength-dependent phase responses remains a major obstacle for practical metalens applications in the visible regime. In this work, we present the design and analysis of an achromatic metalens operating in the visible spectrum using silicon nitride (Si3N4) dielectric metasurfaces. The metalens employs a phase-engineering strategy based on propagation-phase modulation of polarization-independent nanostructures. By constructing a unit-cell phase library through systematic parameter scanning, the phase responses at different wavelengths are accurately mapped. An interleaved arrangement strategy is introduced, where meta-atoms designed for different target wavelengths are alternately distributed within a single metalens aperture, enabling multi-wavelength phase compensation without increasing the structural complexity. Numerical simulations demonstrate that the proposed metalens achieves near-coincident focal positions across a broad visible-wavelength range. The focal length variation is significantly suppressed compared with conventional single-wavelength metalenses. The metalens exhibits stable focusing behavior with symmetric focal spots, consistent focal sizes, and improved chromatic tolerance. The results confirm that the interleaved design effectively mitigates chromatic focal shift while maintaining high transmission efficiency. This study provides a practical and scalable approach to achieving achromatic focusing in visible-wavelength metalenses. The proposed Si3N4-based interleaved design offers strong potential for compact imaging systems, integrated photonics, and visible-light optical devices.

Physical Sciences Forum doi: 10.3390/psf2026014007

Authors: Jacques B. Ngoua Ndong Avele Vladimir K. Orlov

Our research explores the sensitivity of satellite constellations to fluctuations in the quantum gravitational field, with the aim of quantifying their potential impact on critical operations and precision measurement. The main objective is to quantify the potential impact of hypothetical quantum effects on the precise orbital dynamics of satellite constellations. The methods employed will involve the development of a sophisticated computational model using the quantum toolbox QuTIP in Python. Our model will simulate the trajectories of various satellite constellation configurations in two distinct gravitational frameworks: one based on classical general relativity and the other incorporating theoretical models of quantum gravitational fluctuations. The main task is to meticulously analyze and compare the resulting differences in key orbital parameters, including position, velocity, and orbital period, between these two scenarios. This comparative analysis is crucial in determining whether the minute stochastic perturbations resulting from quantum gravity could accumulate over time and measurably affect the performance and objectives of current and future high-precision satellite missions, such as those related to Earth observation, global navigation satellite systems (GNSSs), or space-based gravitational wave detectors. The expected results have provided essential insights into the potential need to integrate quantum gravitational corrections into very high-precision astrodynamics and will contribute significantly to ongoing theoretical and experimental efforts to unify quantum mechanics and general relativity at the macroscopic scale.

Physical Sciences Forum doi: 10.3390/psf2026014005

Authors: Satenik Ghazaryan Georges Alecian

Observational and statistical studies of chemically peculiar (CP) stars show that the origin of heavy radioactive element overabundances in their atmospheres remains unclear. We investigate whether such anomalies can be explained by supernova explosions in binary or multiple systems by searching for CP star–pulsar pairs via Galactic traceback analysis. We examine 529 CP stars and 28 pulsars (ages ≤ 100 Myr) and identify several candidate pairs that may have shared a common origin. Comparison with young stellar clusters suggests possible parent groups. Further work is needed to constrain CP star ages and assess the statistical significance of the proposed supernova scenarios.

Physical Sciences Forum doi: 10.3390/psf2026014006

Authors: Komiljon Tillaboev Ikram Tadjibaev

Dwarf galaxies, although intrinsically faint and containing only modest stellar populations, provide an unusually sensitive testing ground for understanding how structure emerges in a cosmological context. Their shallow gravitational potentials make them particularly responsive to environmental influences and internal feedback, allowing researchers to probe physical processes that are harder to isolate in larger systems. Over the past decade, advances in numerical modeling—ranging from finely resolved hydrodynamic calculations to large-volume N-body suites—have offered increasingly detailed views of their kinematic evolution, star formation cycles, and dark matter configurations. Modern simulations now reproduce several of the empirical relationships observed in nearby dwarfs, including trends connecting mass, size, chemical enrichment, and luminosity. However, uncertainties in how feedback is implemented still produce noticeable variation among models. A long-standing tension involves the predicted shape of central dark matter profiles. Many simulations generate steep cusps, even though observations frequently point to shallower cores. Energetic stellar activity has been proposed as a mechanism for reshaping these regions, yet its effectiveness depends sensitively on resolution and feedback prescriptions. Another unresolved issue concerns the unexpectedly small number of known satellites in the Local Group compared with the abundance of low-mass halos in ΛCDM predictions. Recent work indicates that many such halos may host extremely faint systems that elude current surveys. Large simulation programs such as FIRE, APOSTLE, and NIHAO pursue these questions with differing assumptions and numerical strategies. Each captures certain aspects of dwarf galaxy evolution, but none fully replicates the diversity seen observationally. Considering results from multiple frameworks remains essential for constructing a comprehensive picture of how these small galaxies form, evolve, and interact with their environments.

Physical Sciences Forum doi: 10.3390/psf2026013011

Authors: Abid Husain Haris Kunari

Previously reported atomic data for Cs XI, including spectral lines, wavelengths, energy levels, and transition probabilities, were collected and systematically analyzed. The present theoretical investigation was supported by extensive calculations performed for Cs XI using the pseudo-relativistic Hartree–Fock (HFR) method with configuration interaction, as implemented in Cowan’s codes. In this work, all previously reported energy levels and their allowed transition assignments were confirmed. A critically evaluated dataset is presented, including optimized energy levels with their uncertainties, observed and Ritz wavelengths with their corresponding uncertainties, and theoretical transition probabilities with estimated uncertainties.

Physical Sciences Forum doi: 10.3390/psf2026014004

Authors: Bachir Moussaoui Amal Ait El Djoudi Mohamed Amine Lakehal

We investigate finite-size effects on the density-driven deconfinement phase transition in Quantum Chromodynamics (QCD) using a phase coexistence model of hadronic and quark–gluon plasma (QGP) phases in a finite size. The QGP is described via the MIT bag model, incorporating the color-singletness constraint to enforce exact color neutrality. In this study, we analyze the first- and second-order derivatives of the order parameter, defined as the mean hadronic volume fraction h , with respect to the quark chemical potential ( μ ) at fixed temperature ( T ) and for several system volumes V , to identify the effective transition point. Our results show that the effective quark chemical potential μ c ( V ) increases as the volume decreases, and the transition becomes progressively smoother, with a width δ μ ( V ) that broadens with decreasing volume.

Physical Sciences Forum doi: 10.3390/psf2026013009

Authors: Daniel Garcia Tomás Campante Ricardo Ferreira da Silva Luís Leitão Jorge Sampaio José Pires Marques

Recombination processes heavily influence the ionization balance and level populations of Non-Local Thermodynamic Equilibrium (Non-LTE) plasmas. This is especially relevant for kilonovae, whose ejecta rapidly evolves into a Non-LTE regime. Collisional-radiative models become necessary, but are limited by the scarcity of data for heavy r-process elements. In this work, we present radiative recombination (RR) rate coefficients for Pm-like tungsten (W XIV) using the Flexible Atomic Code (FAC). W XIV makes a reliable benchmark for two primary reasons: tungsten is one of the heavy elements best characterized across its ionization sequence, providing a wealth of available data for comparison, and this charge state shares the complicated open f-shell structure of key r-process elements, consequently presenting similar challenges that emerge when executing this type of calculation. Validating our approach here provides the foundation for extending this methodology to the lanthanide sequence. The calculations performed in this work show good agreement with previous relevant works. Additionally, an RR calculation with a set of configurations that captured the low-lying energy structure of the recombined ion was performed. The results remain within the same order of magnitude as the standard method across the 10 3 to 10 9 K temperature range.

Physical Sciences Forum doi: 10.3390/psf2026013008

Authors: Hashmitha Sugumar Nilakantha Meher

A two-level atom interacting with a cavity field initialized in a coherent state exhibits the collapse and revival features in its population inversion dynamics under the Jaynes–Cummings interaction. When the cavity field is prepared in a number-state filtered coherent state, obtained by filtering the number state | m 〉 from a coherent state | α 〉 , we observe micro-Rabi oscillations in the collapse region instead of a perfect collapse. We find that the Rabi frequency of these micro oscillations depends only on the filtered photo-number m, whereas the amplitude depends on both m and α . This dependence enables one to infer the filtered number state from the coherent-state distribution by analyzing the time evolution of the atomic population inversion. Conversely, the amplitude and frequency of the micro-Rabi oscillations can be tuned by filtering an appropriate number state. Furthermore, we investigate the effect of filtering multiple number states from a coherent state on the atomic population inversion dynamics.

Physical Sciences Forum doi: 10.3390/psf2026015001

Authors: Martin Hessling Ben Sicks Klea Lila Felix Capanni Vinzent Forstmeier

Introduction: Biofilms are communities of microorganisms on wet or moist surfaces that are difficult to remove and can repeatedly release microorganisms. This causes significant problems in many areas, such as food technology and medicine, as biofilms can cause fatal infections in patients. One approach that has not yet been extensively researched is to prevent biofilm formation on surfaces by irradiating them with visible blue or violet light, which is known for its antimicrobial effect on planktonic bacteria. Methods: Various bacterial suspensions (Bacillus subtilis, Pseudomonas stutzeri, Streptococcus cristatus and Pseudomonas syringae) were placed in 24-well MTPs (microtiter plates) and irradiated with violet or blue light for up to 6 days. During this time, they were shaken at room temperature at a frequency of 100 rpm. The irradiation intensities were 0, 10 or 20 mW/cm2. After each day, the bacterial suspension was aspirated and the biofilm at the bottom of the well was assessed by absorption measurement and microscopy images. Results: Besides B. subtilis, thin biofilms were found on all well bottoms after 24 h, even on the irradiated microtiter plates. In general, however, the biofilm absorption/scattering was lower on the irradiated MTPs than on the non-irradiated ones. For B. subtilis, irradiation prevented biofilm formation during the observation period. Surprisingly, in most experiments, blue light had a stronger effect than violet light, which does not correspond to the experiences with planktonic bacteria. Conclusion: Violet and blue light exhibits a positive effect on suppressing the formation of new biofilm, but the applied maximum irradiance of 20 mW/cm2 was unable to prevent the formation of new biofilm for most of the examined bacteria. Further investigations with higher irradiance levels are necessary to determine whether it is possible to inhibit the formation of new biofilm altogether.

Physical Sciences Forum doi: 10.3390/psf2026014003

Authors: Ekaterina Pozdeeva Tatiana Pozdeeva Sergey Vernov

We consider a two-field chiral cosmological model of inflation connected with induced gravity. It is shown that the constructed inflationary model does not contradict recent observational data from the Atacama Cosmology Telescope (ACT) and the Dark Energy Spectroscopic Instrument (DESI). The corresponding values of the model parameters are determined. This model is suitable for the formation of primordial black holes (PBHs). The estimation of PBH masses allows us to consider these PBHs as dark matter candidates.

Physical Sciences Forum doi: 10.3390/psf2026013010

Authors: Sergey Zaytsev Lorenzo Ugo Ancarani Darya Zaytseva Alexander Zaytsev Yury Popov Konstantin Kouzakov

We investigate the 75 keV proton impact ionization of helium. The convoluted quasi-Sturmian approach is extended to treat both the direct ionization and the electron capture to the continuum by proposing an ansatz for the Coulomb three-body Green’s function operator, for the kernel of which the leading asymptotic form contains the wave function of the 3C model. The resulting 3C-like model is tested numerically. Fully differential cross-sections are calculated for different energy-loss regimes and compared with recent experimental data.

Physical Sciences Forum doi: 10.3390/psf2026013007

Authors: Zaid Mustafa Haris Kunari

Accurate and precise spectral data for singly ionized silicon (Si ii) are important for numerous applications. In this work, we present high-resolution Fourier transform IR emission spectroscopy of Si ii, using spectra recorded with the 1 m FTS at the Kitt Peak National Observatory. A total of 18 lines were measured in the region 780 nm to 4000 nm, and an immediate comparison of our data with the NIST ASD shows that our measurements offer at least a tenfold improvement in precision.

Physical Sciences Forum doi: 10.3390/psf2026014002

Authors: Değer Sofuoğlu Bhupendra Kumar Shukla Aroonkumar Beesham

In this study, we propose a novel framework using a logarithmic parametrization of the deceleration parameter q ( z ) . This specific form facilitates the reconstruction of the expansion history in a closed analytical form, providing crucial theoretical tractability. We aim to constrain the free parameters of this logarithmic model using a robust combination of datasets (CC, Pantheon+ SNe Ia, BAO, and the R19 prior) to reconstruct fundamental dynamic quantities and determine the transition redshift ( z t r ) with high precision.

Physical Sciences Forum doi: 10.3390/psf2026014001

Authors: Lorenzo Iorio

The 3rd International Online Conference on Universe, organized by the journal Universe, was held from 4 to 6 March 2026. It collected distinguished researchers from all over the world who showcased their latest researches on topics like Gravitation and Cosmology, Field Theory, Quantum Gravity, High Energy, Nuclei and Particle Physics, Foundation of Quantum Mechanics, Astrophysics and Compact Objects, Solar and Stellar Physics, and Astroparticle Physics. Both posters and talks were at the forefront of the current research, and the discussions were often vibrant and penetrating with several talks that attracted many stimulating inquiries. This report, collecting the extended abstracts of the participants, gives an idea of the tone of the conference.

Physical Sciences Forum doi: 10.3390/psf2026013006

Authors: Malvika Singh Richa Paijwar Rinku Sharma

We present a detailed study of the atomic structure and radiative properties of highly charged neon-like selenium (Se XXV), motivated by its importance in plasma diagnostics, fusion research, and astrophysical spectroscopy. We calculated excitation energies and radiative parameters for the 50 lowest levels of fine structure using a fully relativistic multiconfiguration Dirac–Fock approach. We calculated transition wavelengths, radiative transition rates, oscillator strengths, and line strengths for electric dipole, magnetic dipole, electric quadrupole, and magnetic quadrupole transitions among the specified levels. We also evaluated Einstein coefficients for spontaneous and stimulated emission, transition dipole moments, and radiative lifetimes of the low-lying states. To validate the results, we performed independent relativistic calculations using an alternative theoretical method and compared the datasets to examine internal consistency. The calculated excitation energies and radiative parameters agree well with values reported in the National Institute of Standards and Technology database (NIST) and other published theoretical results. The agreement between the independent approaches confirms the consistency of the present dataset. These results provide reliable atomic data for spectral line identification and quantitative plasma modeling in laboratory and astrophysical environments and support ongoing experimental and diagnostic studies of highly charged ions.

Physical Sciences Forum doi: 10.3390/psf2026013005

Authors: Ricardo Ferreira da Silva Luis Leitão Andreas Flörs Tomás Campante Daniel Garcia Jorge Sampaio Gabriel Martínez-Pinedo José Pires Marques

The modelling of kilonova spectra, particularly in the late-time nebular phase, relies heavily on accurate atomic data. While significant progress has been made regarding energy levels and radiative transition rates for r-process elements, data for collisional processes remains scarce. Current models often rely on the Van Regemorter and Axelrod approximations for effective collision strengths. In this work we present the calculation of electron-impact excitation (EIE) collision strengths for relevant r-process elements. We employ the Independent-Process, Isolated-Resonance Distorted-Wave (IPIRDW) approximation to account for the contribution of resonant excitation, which is crucial at the low temperatures characteristic of kilonovae. We demonstrate the validity of our method by benchmarking against R-Matrix calculations for Te iii, finding good agreement while maintaining a significantly lower computational cost. We focus on the relevant 2.1 μm feature and estimate a mass of 2.6× 10 − 3 M ⊙ using our IPIRDW data for EIE effective collision strengths, compatible with other recent estimations using R-Matrix data.

Physical Sciences Forum doi: 10.3390/psf2026013002

Authors: Emiliano Acebal Sebastian Otranto

In this work, we study the single ionization of H2O and C4H8O by electron impact. Fully differential cross sections are calculated by means of two distorted wave models which vary in the single-centre approximation applied to the interaction of the continuum electrons with the residual molecular ion. Dependence on the molecular target orientation is analyzed before performing an average procedure to benchmark with experimental data. The present results suggest that structures in non-oriented triple differential cross sections do not directly reflect the patterns inferred from a limited set of particular orientations, demanding an extensive averaging procedure.

Physical Sciences Forum doi: 10.3390/psf2026013004

Authors: Nelson D. Cariatore Sebastian Otranto

We report an enhanced Classical Trajectory Monte Carlo (CTMC) approach developed to study state-selective charge exchange in collisions between multiply charged ions and H2 molecules. The model combines two hydrogenic three-body formulations—originally designed to improve the H( 1 s ) radial distribution—within the five-body CTMC framework introduced by Wood and Olson. The new schemes, termed E-CTMC and Z-CTMC, extend the electronic density of the target to larger distances, providing a more accurate representation of the molecular system. Calculations for 2 to 100 keV/u Ne9+ and O6+ projectiles at low and intermediate impact energies are benchmarked against recent laboratory data and the Multichannel Landau–Zener method. The Z-CTMC approach reproduces the observed energy-dependent shift of the most populated n levels, showing the closest overall agreement with the experiments. Complementary simulations for different projectiles show that discrepancies among the CTMC variants grow with increasing projectile charge and lower impact energies, emphasizing the need for further experimental measurements involving highly charged ions. The present formulation offers a consistent framework for analyzing charge-exchange processes relevant to laboratory and astrophysical plasmas.

Physical Sciences Forum doi: 10.3390/psf2026013003

Authors: Tomás Campante Ricardo Ferreira da Silva Luís Leitão Daniel Garcia Jorge Miguel Sampaio José Manuel Pires Marques

With the detection of kilonova AT2017gfo, (binary) neutron star mergers emerged as possible astrophysical sites for heavy element nucleosynthesis via r-process. To verify this claim, it is key to identify elements such as lanthanides and actinides in kilonovae spectra. Theoretical calculations arise as a solution to fill the scarcity of experimental atomic data to perform this identification. This work presents theoretical calculations with the AUTOSTRUCTURE atomic code for Ho, Er, Tm, Yb and Lu singly and doubly ionised, and benchmarks them against experimental data. The similarity between these theoretical calculations and experimental data was quantified via a mean absolute relative error (MARE), which showed that the calculations yield an average MARE of 58.7% and 56.7% for the singly and doubly ionised species, respectively.

Physical Sciences Forum doi: 10.3390/psf2026013001

Authors: Blake Gerardo Pérez Renata Della Picca Juan Martín Randazzo

In this work, we address the study of a confined atom in spherical cavities through the Pauli–Fierz Hamiltonian. In this approximation the spherical transverse modes of the field are considered in the Coulomb gauge and in the second quantization formalism. In the present contribution, the longitudinal field is considered through the Hartree potential, an interaction between particles and between the particles and the conductive walls, obtained from the electrostatic energy density and consistent with the boundary conditions imposed to the transversal components. The self-energy states of the coupled system are written in terms of a series of separable field–matter orbitals, in a configuration interaction scheme.

Physical Sciences Forum doi: 10.3390/psf2025012019

Authors: Geert Verdoolaege

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Physical Sciences Forum doi: 10.3390/psf2025011005

Authors: Sergey V. Uchaikin Boris I. Ivanov Arjan F. van Loo Yasunobu Nakamura MinSu Ko Jinmyeong Kim Saebyeok Ahn Seonjeong Oh Yannis K. Semertzidis SungWoo Youn

We present the development of two complementary amplifier architectures for axion haloscope experiments, based on two types of Josephson Parametric Amplifiers (JPAs). The first employs a multi-chip module of flux-driven JPAs in a parallel–series configuration, enabling near quantum-limited amplification over an extended tunable range of between 1.2 and 1.5 GHz. The second design features a lumped-element JPA, offering continuous tunability across a wide frequency range from 2.4 to 4 GHz. Both approaches demonstrate near-quantum-limited noise performance and are compatible with operation in cryogenic environments. These amplifiers significantly enhance the sensitivity and frequency coverage of axion search experiments, and also provide new opportunities for broadband quantum sensing applications.

Physical Sciences Forum doi: 10.3390/psf2025012018

Authors: Roland Preuss Udo von Toussaint

The modelling of plasma–wall interactions (PWIs) depends on distributions describing the angle and energy distribution of particles scattered at the first wall of fusion devices. Most PWI codes rely on extensive tables based on data from reflection simulations, employing a Monte Carlo method. At first glance, the uncertainty distribution of the data should be assumed Gaussian. However, in order to obtain the resulting particle distribution, the reflected ions are counted within angle sections of the upper hemisphere, which hints to a Poisson uncertainty distribution. In this paper, we let Bayesian model comparison decide which uncertainty model should be taken.

Physical Sciences Forum doi: 10.3390/psf2025012016

Authors: Klaus Regenauer-Lieb Manman Hu Hui Tong Chua Victor Calo Boris Yakobson Evgeny P. Zemskov

The static topology of surface characteristics and active sites in catalysis overlooks a crucial element: the dynamic processes of optimal pattern formation over time and the creation of intermediate structures that enhance reactions. Nature’s principle of coupling reaction and motion in catalytic processes by enzymes or higher organisms offers a new perspective. This work explores a novel theoretical approach by adding the time dimension to optimise topological variations using the Maximum Entropy Production (MEP) assumption. This approach recognises that the catalyst surface is not an unchanging energy landscape but can change dynamically. The time-dependent transport problem of molecules is here interpreted by a non-equilibrium model used for modelling and predicting dynamic pattern formation in excitable media, a class of active matter requiring an activation threshold. We present a nonlocal reaction–cross-diffusion (RXD) formulation of catalytic reactions that can capture the catalyst’s interaction with the target molecule in space and time. The approach provides a theoretical basis for future deep learning models and multiphysics upscaling of catalysts and their support structures across multiphysics fields. The particular advantage of the RXD approach is that it allows each scale to investigate dynamic pattern-forming processes using linear and nonlinear stability analysis, thus establishing a rule base for developing new catalysts.

Physical Sciences Forum doi: 10.3390/psf2025012017

Authors: Robert K. Niven Laurent Cordier Ali Mohammad-Djafari Markus Abel Markus Quade

Consider the dynamical system x ˙ = f ( x ) , where x ∈ R n is the state vector, x ˙ is the time or spatial derivative, and f is the system model. We wish to identify unknown f from its time-series or spatial data. For this, we propose a Bayesian framework based on the maximum a posteriori (MAP) point estimate, to give a generalized Tikhonov regularization method with the residual and regularization terms identified, respectively, with the negative logarithms of the likelihood and prior distributions. As well as estimates of the model coefficients, the Bayesian interpretation provides access to the full Bayesian apparatus, including the ranking of models, the quantification of model uncertainties, and the estimation of unknown (nuisance) hyperparameters. For multivariate Gaussian likelihood and prior distributions, the Bayesian formulation gives a Gaussian posterior distribution, in which the numerator contains a Mahalanobis distance or “Gaussian norm”. In this study, two Bayesian algorithms for the estimation of hyperparameters—the joint maximum a posteriori (JMAP) and variational Bayesian approximation (VBA)—are compared to the popular SINDy, LASSO, and ridge regression algorithms for the analysis of several dynamical systems with additive noise. We consider two dynamical systems, the Lorenz convection system and the Shil’nikov cubic system, with four choices of noise model: symmetric Gaussian or Laplace noise and skewed Rayleigh or Erlang noise, with different magnitudes. The posterior Gaussian norm is found to provide a robust metric for quantitative model selection—with quantification of the model uncertainties—across all dynamical systems and noise models examined.

Physical Sciences Forum doi: 10.3390/psf2025012015

Authors: Richard Fuchs Jakob Knollmüller Lukas Heinrich

Building appropriate models is crucial for imaging tasks in many fields but often challenging due to the richness of the systems. In radio astronomy, for example, wide-field observations can contain various and superposed structures that require different descriptions, such as filaments, point sources or compact objects. This work presents an automatic pipeline that iteratively adapts probabilistic models for such complex systems in order to improve the reconstructed images. It uses the Bayesian imaging library NIFTy, which is formulated in the language of information field theory. Starting with a preliminary reconstruction using a simple and flexible model, the pipeline employs deep learning and clustering methods to identify and separate different objects. In a further step, these objects are described by adding new building blocks to the model, allowing for a component separation in the next reconstruction step. This procedure can be repeated several times for refinement to iteratively improve the overall reconstruction. In addition, the individual components can be modeled at different resolutions allowing us to focus on important parts of the emission field without getting computationally too expensive.

Physical Sciences Forum doi: 10.3390/psf2025012014

Authors: Nasrin Taghavi Robert K. Niven Matthias Kramer David J. Paull

Groundwater vulnerability assessment (GVA) is critical for understanding contaminant migration into groundwater systems, yet conventional methods often overlook its probabilistic nature. Bayesian inference offers a robust framework using Bayes’ rule to enhance decision-making through posterior probability calculations. This study introduces inverse Bayesian methods for GVA using spatial-series data, focusing on nitrate concentrations in groundwater as an indicator of groundwater vulnerability in agricultural catchments. Using the joint maximum a-posteriori (JMAP) and variational Bayesian approximation (VBA) algorithms, the advantages of the Bayesian framework over traditional index-based methods are demonstrated for GVA of the Burdekin Basin, Queensland, Australia. This provides an evidence-based methodology for GVA which enables model ranking, parameter estimation, and uncertainty quantification.

Physical Sciences Forum doi: 10.3390/psf2025012013

Authors: Jeffrey De Rycke Alfredo Pironti Marco Ariola Antonio Quercia Geert Verdoolaege

Magnetic confinement nuclear fusion offers a promising solution to the world’s growing energy demands. The DEMO reactor presented here aims to bridge the gap between laboratory fusion experiments and practical electricity generation, posing unique challenges for magnetic plasma diagnostics due to limited space for diagnostic equipment. This study employs Bayesian inference and Gaussian process modeling to integrate data from pick-up coils, flux loops, and saddle coils, enabling a qualitative estimation of the plasma current density distribution relying on only external magnetic measurements. The methodology successfully infers total plasma current, plasma centroid position, and six plasma–wall gap positions, while adhering to DEMO’s stringent accuracy standards. Additionally, the interchangeability between normal pick-up coils and saddle coils was assessed, revealing a clear preference for saddle coils. Initial steps were taken to utilize Bayesian experimental design for optimizing the orientation (normal or tangential) of pick-up coils within DEMO’s design constraints to improve the diagnostic setup’s inference precision. Our approach indicates the feasibility of Bayesian integrated data analysis in achieving precise and accurate probability distributions of plasma parameter crucial for the successful operation of DEMO.

Physical Sciences Forum doi: 10.3390/psf2025012012

Authors: Thomas Loredo Tamás Budavári David Kent David Ruppert

Cosmic demographics—the statistical study of populations of astrophysical objects—has long relied on tools from multivariate statistics for analyzing data comprising fixed-length vectors of properties of objects, as might be compiled in a tabular astronomical catalog (say, with sky coordinates, and brightness measurements in a fixed number of spectral passbands). But beginning with the emergence of automated digital sky surveys, ca. 2000, astronomers began producing large collections of data with more complex structures: light curves (brightness time series) and spectra (brightness vs. wavelength). These comprise what statisticians call functional data—measurements of populations of functions. Upcoming automated sky surveys will soon provide astronomers with a flood of functional data. New methods are needed to accurately and optimally analyze large ensembles of light curves and spectra, accumulating information both along individual measured functions and across a population of such functions. Functional data analysis (FDA) provides tools for statistical modeling of functional data. Astronomical data presents several challenges for FDA methodology, e.g., sparse, irregular, and asynchronous sampling, and heteroscedastic measurement error. Bayesian FDA uses hierarchical Bayesian models for function populations, and is well suited to addressing these challenges. We provide an overview of astronomical functional data and some key Bayesian FDA modeling approaches, including functional mixed effects models, and stochastic process models. We briefly describe a Bayesian FDA framework combining FDA and machine learning methods to build low-dimensional parametric models for galaxy spectra.

Physical Sciences Forum doi: 10.3390/psf2025011004

Authors: Josep Maria Batllori Michael H. Frosz Dieter Horns Marios Maroudas

Axions and axion-like particles (ALPs) are well-motivated dark matter (DM) candidates that couple with photons in external magnetic fields. The parameter space around m a ∼ 50 meV remains largely unexplored by haloscope experiments. We present the first prototype of Weakly Interacting Sub-eV Particles (WISP) Searches on a Fiber Interferometer (WISPFI), a table-top, model-independent scheme based on resonant photon–axion conversion in a hollow-core photonic crystal fiber (HC-PCF) integrated into a Mach–Zehnder interferometer (MZI). Operating near a dark fringe with active phase-locking, combined with amplitude modulation, the interferometer converts axion-induced photon disappearance into a measurable signal. A 2 W, 1550 nm laser is coupled with a 1 m-long HC-PCF placed inside a ∼2 T permanent magnet array, probing a fixed axion mass of m a ≃ 49 meV with a projected sensitivity of g a γ γ ≳ 1.3× 10 − 9 GeV−1 for a measurement time of 30 days. Future upgrades, including pressure tuning of the effective refractive index and implementation of a Fabry–Pérot cavity, could extend the accessible mass range and improve sensitivity, establishing WISPFI as a scalable platform to explore previously inaccessible regions of the axion parameter space.

Physical Sciences Forum doi: 10.3390/psf2025011003

Authors: Pierre Pugnat Rafik Ballou Philippe Camus Guillaume Donnier-Valentin Thierry Grenet Ohjoon Kwon Jérôme Lacipière Mickaël Pelloux Rolf Pfister Yannis K. Semertzidis Arthur Talarmin Jérémy Vessaire SungWoo Youn

Two outstanding problems of particle physics and cosmology, namely the strong-CP problem and the nature of dark matter, can be solved with the discovery of a single new particle, the axion. The modular high magnetic field and flux hybrid magnet platform of LNCMI-Grenoble, which was recently put in operation up to 42 T, offers unique opportunities for axion/axion-like particle search using Sikivie-type haloscopes. In this paper, the focus will be on the 350–600 MHz frequency range corresponding to the 1–3 μeV axion mass range requiring a large-bore RF-cavity. It will be built by DMAG and integrated within the large-bore superconducting hybrid magnet outsert, providing a central magnetic field up to 9 T in 812 mm warm bore diameter. The progress achieved by Néel Institute in the design of the complex cryostat with its double dilution refrigerators to cooldown below 50 mK the ultra-light Cu RF-cavity of 650 mm inner diameter and the first stage of the RF measurement chain are presented. Perspectives for the targeted sensitivity, assuming less than 2-year integration time, are recalled.

Physical Sciences Forum doi: 10.3390/psf2025011002

Authors: Gulden Othman Robert H. Hadfield Katharina-Sophie Isleif Friederike Januschek Axel Lindner Manuel Meyer Dmitry Morozov Devendra Kumar Namburi Elmeri Rivasto José Alejandro Rubiera Gimeno Christina Schwemmbauer

Superconducting Transition Edge Sensors (TESs) are a promising technology for fundamental physics applications due to their low dark count rates, excellent energy resolution, and high detection efficiency. On the DESY campus, we have been developing a program to characterize cryogenic quantum sensors for fundamental physics applications, particularly focused on TESs. We currently have two fully equipped dilution refrigerators that enable simultaneous TES characterization and fundamental physics searches. In this paper, we summarize the current status of our TES characterization, including recent calibration efforts and efficiency measurements, as well as simulations to better understand TES behavior and its backgrounds. Additionally, we summarize some physics applications that we are already exploring or planning to explore. We will give preliminary projections on a direct dark matter search with our TES, where exploiting low-threshold electron scattering in superconducting materials allows us to search for sub-MeV-scale dark matter. We are also working toward performing a measurement of the even-number photon distribution (beyond one pair) of a quantum-squeezed light source. Finally, if it proves to meet the requirements, our TES detector may be used as a second, independent detection system to search for an axion signal at the ALPS II experiment.

Physical Sciences Forum doi: 10.3390/psf2025012011

Authors: Fernando Corrêa Julio Michael Stern Rafael Bassi Stern

In this article, we present an extension of the Full Bayesian Significance Test (FBST) for nonparametric settings, termed NP-FBST, which is constructed using the limit of finite dimension histograms. The test statistics for NP-FBST are based on a plug-in estimate of the cross-entropy between the null hypothesis and a histogram. This method shares similarities with Kullback–Leibler and entropy-based goodness-of-fit tests, but it can be applied to a broader range of hypotheses and is generally less computationally intensive. We demonstrate that when the number of histogram bins increases slowly with the sample size, the NP-FBST is consistent for Lipschitz continuous data-generating densities. Additionally, we propose an algorithm to optimize the NP-FBST. Through simulations, we compare the performance of the NP-FBST to traditional methods for testing uniformity. Our results indicate that the NP-FBST is competitive in terms of power, even surpassing the most powerful likelihood-ratio-based procedures for very small sample sizes.

Physical Sciences Forum doi: 10.3390/psf2025012010

Authors: Ali Mohammad-Djafari Ning Chu Li Wang Caifang Cai Liang Yu

Neural Networks (NNs) have been used in many areas with great success. When an NN’s structure (model) is given, during the training steps, the parameters of the model are determined using an appropriate criterion and an optimization algorithm (training). Then, the trained model can be used for the prediction or inference step (testing). As there are also many hyperparameters related to optimization criteria and optimization algorithms, a validation step is necessary before the NN’s final use. One of the great difficulties is the choice of NN structure. Even if there are many “on the shelf” networks, selecting or proposing a new appropriate network for a given data signal or image processing task, is still an open problem. In this work, we consider this problem using model-based signal and image processing and inverse problems methods. We classify the methods into five classes: (i) explicit analytical solutions, (ii) transform domain decomposition, (iii) operator decomposition, (iv) unfolding optimization algorithms, (v) physics-informed NN methods (PINNs). A few examples in each category are explained.

Physical Sciences Forum doi: 10.3390/psf2025012009

Authors: Vasuda Trehan Kevin H. Knuth M. J. Way

The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model for exoplanets. The eventual model will be based on both collected observational spectra and synthetic spectral data generated by the ROCKE-3D general circulation model (GCM) developed by the climate modeling program at NASA’s Goddard Institute for Space Studies (GISS). In this initial study, spline curves are used to describe the bin heights of simulated atmospheric absorption spectra as a function of one of the values of the planetary parameters. Bayesian Adaptive Exploration is then employed to identify areas of the planetary parameter space for which more data are needed to improve the model. The resulting system will be used as a forward model so that planetary parameters can be inferred given a planet’s atmospheric absorption spectrum. This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability.

Physical Sciences Forum doi: 10.3390/psf2025012008

Authors: Lune Maillard Fabio Finocchi César Godinho Martino Trassinelli

Lennard-Jones clusters, while an easy system, have a significant number of non equivalent configurations that increases rapidly with the number of atoms in the cluster. Here, we aim at determining the cluster partition function; we use the nested sampling algorithm, which transforms the multidimensional integral into a one-dimensional one, to perform this task. In particular, we use the nested_fit program, which implements slice sampling as search algorithm. We study here the 7-atom and 36-atom clusters to benchmark nested_fit for the exploration of potential energy surfaces. We find that nested_fit is able to recover phase transitions and find different stable configurations of the cluster. Furthermore, the implementation of the slice sampling algorithm has a clear impact on the computational cost.

Physical Sciences Forum doi: 10.3390/psf2025012007

Authors: Giorgio Sonnino

This letter aims to investigate thermodynamic processes in small systems in the Onsager region by showing that fundamental quantities such as total entropy production can be discretized on the mesoscopic scale. Even thermodynamic variables can conjugate to thermodynamic forces, and thus, Glansdorff–Prigogine’s dissipative variable may be discretized. The canonical commutation rules (CCRs) valid at the mesoscopic scale are postulated, and the measurement process consists of determining the eigenvalues of the operators associated with the thermodynamic quantities. The nature of the quantized quantity β , entering the CCRs, is investigated by a heuristic model for nano-gas and analyzed through the tools of classical statistical physics. We conclude that according to our model, the constant β does not appear to be a new fundamental constant but corresponds to the minimum value.

Physical Sciences Forum doi: 10.3390/psf2025012005

Authors: Johann Fredrik Jadebeck Wolfgang Wiechert Katharina Nöh

Bayesian analysis is particularly useful for inferring models and their parameters given data. This is a common task in metabolic modeling, where models of varying complexity are used to interpret data. Nested sampling is a class of probabilistic inference algorithms that are particularly effective for estimating evidence and sampling the parameter posterior probability distributions. However, the practicality of nested sampling for metabolic network inference has yet to be studied. In this technical report, we explore the amalgamation of nested sampling, specifically diffusive nested sampling, with reversible jump Markov chain Monte Carlo. We apply the algorithm to two synthetic problems from the field of metabolic flux analysis. We present run times and share insights into hyperparameter choices, providing a useful point of reference for future applications of nested sampling to metabolic flux problems.

Physical Sciences Forum doi: 10.3390/psf2025012003

Authors: Carola S. Heinzel Johann F. Jadebeck Elisabeth Zelle Johannes Seiffarth Katharina Nöh

Better understanding of the fitness and flexibility of microbial platform organisms is central to biotechnological process development. Live-cell experiments uncover the phenotypic heterogeneity of living cells, emerging even within isogenic cell populations. However, how this observed heterogeneity in growth relates to the variability of intracellular processes that drive cell growth and division is less understood. We here approach the question, how the observed phenotypic variability in single-cell growth rates links to metabolic processes, specifically intracellular reaction rates (fluxes). To approach this question, we employ the Maximum Entropy (MaxEnt) principle that allows us to bring together the phenotypic solution space, derived from metabolic network models, to single-cell growth rates observed in live-cell experiments. We apply the computational machinery to first-of-its-kind data of the microorganism Corynebacterium glutamicum, grown on different substrates under continuous medium supply. We compare the MaxEnt-based estimates of metabolic fluxes with estimates obtained by assuming that the average cell operates at its maximum growth rate, which is the current predominant practice in biotechnology.

Physical Sciences Forum doi: 10.3390/psf2025012002

Authors: Rodrigo F. L. Lassance Julio M. Stern Rafael B. Stern

In Bayesian analysis, testing for linearity requires placing a prior to the entire space of potential regression functions. This poses a problem for many standard tests, as assigning positive prior probability to such a hypothesis is challenging. The Full Bayesian Significance Test (FBST) sidesteps this issue, standing out for also being logically coherent and offering a measure of evidence against H 0 , although its application to nonparametric settings is still limited. In this work, we use Gaussian process priors to derive FBST procedures that evaluate general linearity assumptions, such as testing the adherence of data and performing variable selection to linear models. We also make use of pragmatic hypotheses to verify if the data might be compatible with a linear model when factors such as measurement errors or utility judgments are accounted for. This contribution extends the theory of the FBST, allowing for its application in nonparametric settings and requiring, at most, simple optimization procedures to reach the desired conclusion.

Physical Sciences Forum doi: 10.3390/psf2025012006

Authors: Stefan Behringer Roman V. Belavkin

This paper explores the application of the Value of Information, (VoI), based on the Claude Shannon/Ruslan Stratonovich framework within economic contexts. Unlike previous studies that examine circular settings and strategic interactions, we focus on a non-strategic linear setting. We employ standard economically motivated utility functions, including linear, quadratic, constant absolute risk aversion (CARA), and constant relative risk aversion (CRRA), across various priors of the stochastic environment, and analyse the resulting specific VoI forms. The curvature of these VoI functions play a decisive role in determining whether acquiring additional costly information enhances the efficiency of the decision making process. We also outline potential implications for broader decision-making frameworks.

Physical Sciences Forum doi: 10.3390/psf2025012004

Authors: Aricia Rinkens Rodrigo L. S. Silva Clemens V. Verhoosel Nick O. Jaensson Erik Quaeghebeur

Using Bayesian inference to calibrate constitutive model parameters has recently seen a rise in interest. The Markov chain Monte Carlo (MCMC) algorithm is one of the most commonly used methods to sample from the posterior. However, the choice of which MCMC algorithm to apply is typically pragmatic and based on considerations such as software availability and experience. We compare three commonly used MCMC algorithms: Metropolis-Hastings (MH), Affine Invariant Stretch Move (AISM) and No-U-Turn sampler (NUTS). For the comparison, we use the Kullback-Leibler (KL) divergence as a convergence criterion, which measures the statistical distance between the sampled and the ‘true’ posterior. We apply the Bayesian framework to a Newtonian squeeze flow problem, for which there exists an analytical model. Furthermore, we have collected experimental data using a tailored setup. The ground truth for the posterior is obtained by evaluating it on a uniform reference grid. We conclude that, for the same number of samples, the NUTS results in the lowest KL divergence, followed by the AISM sampler and last the MH sampler.

Physical Sciences Forum doi: 10.3390/psf2025012001

Authors: Fabrice Pautot

When the quantities of interest remain underdetermined a posteriori, we would like to draw inferences for at least one particular solution. Can we do so in a Bayesian way? What is a probability distribution over an underdetermined quantity? How do we get a posterior for one particular solution from a posterior for infinitely many underdetermined solutions? Guided by discrete invariant underdetermined ill-posed inverse problems, we find that a probability distribution over an underdetermined quantity is non-absolutely continuous, partially improper with respect to the initial reference measure but proper with respect to its restriction to its support. Thus, it is necessary and sufficient to choose the prior restricted reference measure to assign partially improper priors using e.g., the principle of maximum entropy and the posterior restricted reference measure to obtain the proper posterior for one particular solution. We can then work with underdetermined models like Hoeffding–Sobol expansions seamlessly, especially to effectively counter the curse of dimensionality within discrete nonparametric inverse problems. We show Singular Bayesian Inference (SBI) at work in an advanced Bayesian optimization application: dynamic pricing. Such a nice generalization of Bayesian–maxentropic inference could motivate many theoretical and practical developments.

Physical Sciences Forum doi: 10.3390/psf2025011001

Authors: Johanna von Oy Maurizio Giannotti

Of the three major axion search experimental strategies, light-shining-through-wall experiments, haloscopes, and helioscopes, this paper focuses on the latter. IAXO, the International AXion Observatory, will be a next-generation helioscope following in the footsteps of previous experiments like SUMICO and CAST. Helioscopes aim to detect axions produced in the Sun, utilizing a magnetic field to couple them to X-ray photons. BabyIAXO represents a near-term step toward IAXO, designed to test custom components while delivering competitive results in axion searches. The experimental components are currently under development and construction. Further research into the applications of BabyIAXO beyond baseline axion searches is being conducted.

Physical Sciences Forum doi: 10.3390/psf2024010010

Authors: Francesco Prudenzano Huabei Jiang Maurizio Ferrari

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Physical Sciences Forum doi: 10.3390/psf2024010009

Authors: Hermann Tetzlaff Martin Wortmann Andrea Ehrmann

Exchange-biased specimens were produced by molecular beam epitaxy (MBE) of ferromagnetic (FM) Co-on-CoO substrates after the substrates had been irradiated by heavy ions to induce defects in the antiferromagnet (AFM). Measurements were obtained at different temperatures for different sample orientations with respect to the external magnetic field. While the EB was relatively small, measurements of the bulk magnetization at low temperatures revealed unusually shaped hysteresis loops. The surface magnetization, however, showed simple, nearly rectangular hysteresis loops. This study focuses on the advantage of complementary information on surface and bulk magnetization from optical and non-optical measurement methods.

Physical Sciences Forum doi: 10.3390/psf2024010008

Authors: Jyoti Chauhan Yogita Kalra Ravindra Kumar Sinha

We report a graded index chalcogenide glass (As2Se3)-based photonic crystal fiber having a solid core. The proposed PCF has ultra-high numerical aperture value reaching up to 1.82 for the explored wavelength range of 1.8–10 μm in the mid-infrared region. The value of numerical aperture increases as the pitch increase from 0.92 to 0.96 to 1 micrometer, at a particular value of wavelength. With this high value of numerical aperture, a PCF is capable of gathering a high amount of light in its core. With negative dispersion reaching up to −2000 ps/km/nm at 4.8 µm, the fiber acts as a dispersion-compensating fiber, with confinement loss being close to zero for higher values of wavelength. The confinement loss of the designed PCF is also significantly less and it decreases as the wavelength increases. Also, the value of dispersion is significantly less due to the regular variation in the size of the holes in the transverse direction, as compared to the design when there is no gradation. The design has been optimized with an appropriate value of the perfectly matched layer to achieve the best results.

Physical Sciences Forum doi: 10.3390/psf2024010007

Authors: Francesco Prudenzano Huabei Jiang Maurizio Ferrari

The 1st International Online Conference on Photonics, centered around the theme of optics and photonics, was held from 14 to 16 October 2024. This conference aimed to highlight and facilitate the utilization of recent advancements in all areas related to optics and photonics, as well as to address complex issues, exchange the latest scientific breakthroughs, and guide the development of future technologies and processes in these fields.

Physical Sciences Forum doi: 10.3390/psf2023009027

Authors: Robert K. Niven

Since the early 20th century, dimensional analysis and similarity arguments have provided a critical tool for the analysis of scientific, engineering, and thermodynamic systems. Traditionally, the resulting dimensionless groups are categorized into those defined by (i) geometric similarity, involving ratios of length scales; (ii) kinematic similarity, involving ratios of velocities or accelerations, and (iii) dynamic similarity, involving ratios of forces. This study considers an additional category based on entropic similarity, with three variants defined by the following: (i) ratios of global or local entropy production terms Π entrop = σ ˙ 1 / σ ˙ 2 or Π ^ entrop = σ ˙ ^ 1 / σ ˙ ^ 2 ; (ii) ratios of entropy flow rates Π entrop = F S , 1 / F S , 2 or magnitudes of entropy fluxes Π ^ entrop = | | j S 1 | | / | | j S 2 | | ; and (iii) the ratio of a fluid velocity to that of a carrier of information Π info = U / c . Given that all phenomena involving work against friction, dissipation, spreading, chemical reaction, mixing, separation, or the transmission of information are governed by the second law of thermodynamics, these are more appropriately analyzed directly in terms of competing entropic phenomena and the dominant entropic regime, rather than indirectly using ratios of forces. This work presents the entropic dimensionless groups derived for a wide range of diffusion, chemical reaction, dispersion, and wave phenomena, revealing an entropic interpretation for many known dimensionless groups and many new dimensionless groups.

Physical Sciences Forum doi: 10.3390/psf2024010006

Authors: Shuaicheng Li Jie Cui

In this paper, a novel single-layer dual-band orbital angular momentum (OAM) multiplexed reflective metasurface array antenna is proposed, which can independently generate OAM beams with different modes in the C-band and Ku-band, and complete flexible beam control in each operating band, achieving the generation of an OAM beam with mode l = −1 under oblique incidence at 7G with 94.4% mode purity, and having a wider usable operating bandwidth at 12G with a wide operating bandwidth, and an OAM beam with mode l = +2 is generated under oblique incidence, achieving 82.5% mode purity, which verifies the performance of the unit, makes preparations for the next research, and provides new possibilities for communication in more transmission bands and larger channel capacity.

Physical Sciences Forum doi: 10.3390/psf2024010005

Authors: Nora Dassmann Bennet Brockhagen Andrea Ehrmann

Electrospinning can be used to prepare nanofiber mats from diverse polymers and polymer blends. A large area of research is the application of nanofibrous membranes for tissue engineering. Typically, cell adhesion and proliferation as well as the viability of mammalian cells are tested by seeding the cells on substrates, cultivating them for a defined time and finally dyeing them to enable differentiation between cells and substrates under a white light or fluorescence microscope. While this procedure works well for cells cultivated in well plates or petri dishes, other substrates may undesirably also be colored by the dye. Here we show investigations of the optical contrast between dyed CHO DP-12 (Chinese hamster ovary) cells and different electrospun nanofiber mats, dyed with haematoxylin-eosin (H&E), PromoFluor 488 premium, 4,6-diamidino-2-phenylindole (DAPI) or Hoechst 33342, and give the optimum dyeing parameters for maximum optical contrast between cells and nanofibrous substrates.

Physical Sciences Forum doi: 10.3390/psf2024010004

Authors: Isaac Yorke Lars Emil Gutt Peter David Girouard Michael Galili

In this paper we demonstrate a new approach to the measurement of dispersion of light reflected in integrated optical devices. The approach utilizes the fact that light reflected from the end facet of an integrated waveguide will interfere with light reflected from points inside the device under test (DUT), effectively creating a Michelson interferometer. The distance between the measured fringes of this interferometric signal will depend directly on the group delay experienced in the device under test, allowing for fast and easy measurement of waveguide dispersion. This approach has been used to determine the dispersion of a fabricated linearly chirped Bragg gratings waveguide and the result agrees well with the designed value.

Physical Sciences Forum doi: 10.3390/psf2024010003

Authors: Sebastian Deuschl Ben Sicks Helge Moritz Martin Hessling

Public touchscreens, such as those used in automated teller machines or ticket payment systems, which are accessed by different people in a short period of time, could transmit pathogens and thus spread infections. Therefore, the aim of this study was to develop and test a prototype of a touchscreen system for the public sector that disinfects itself quickly and automatically between two users without harming any humans. A quartz pane was installed in front of a commercial 19” monitor, into which 120 UVC LEDs emitted laterally. The quartz plate acted as a light guide and irradiated microorganisms on its surface, but—due to total reflection—not the user in front of the screen. A near-infrared commercial touch frame was installed to recognize touch. The antibacterial effect was tested through intentional staphylococcus contamination. The prototype, composed of a Raspberry Pi microcomputer with a display, a touchscreen, and a touch frame, was developed, and a simple game was programmed that briefly switched on the UVC LEDs between two users. The antimicrobial effect was so strong that 1% of the maximum UVC LED current was sufficient for a 99.9% staphylococcus reduction within 25 s. At 17.5% of the maximum current, no bacteria were observed after 5 s. The residual UVC irradiance at a distance of 100 mm in front of the screen was only 0.18 and 2.8 µW/cm2 for the two currents, respectively. This would allow users to stay in front of the system for 287 or 18 min, even if the LEDs were to emit UVC continuously and not be turned off after a few seconds as in the presented device. Therefore, fast, automatic touchscreen disinfection with UVC LEDs is already possible today, and with higher currents, disinfection durations below 1 s seems to be feasible, while the light guide approach virtually prevents the direct irradiation of the human user.

Physical Sciences Forum doi: 10.3390/psf2024010002

Authors: Muneeb Farooq Francisco Soares Francisco Diaz

The intrinsic third-order nonlinearity in silicon has proven it to be quite useful in the field of quantum optics. Silicon is suitable for producing time-correlated photon pairs that are sources of heralded single-photon states for quantum integrated circuits. A quantum signal source in the form of single photons is an inherent requirement for the principles of quantum key distribution technology for secure communications. Here, we present numerical simulations of a silicon ring with a 6 μ m radius side-coupled with a bus waveguide as the source for the generation of single photons. The photon pairs are generated by exploring the process of degenerate spontaneous four-wave mixing (SFWM). The free spectral range (FSR) of the ring is quite large, simplifying the extraction of the signal/idler pairs. The phase-matching condition is considered by studying relevant parameters like the dispersion and nonlinearity. We optimize the ring for a high quality factor by varying the gap between the bus and the ring waveguide. This is the smallest ring studied for photon pair generation with a quality factor in the order of 10 5 . The width of the waveguides is chosen such that the phase-matching condition is satisfied, allowing for the propagation of fundamental modes only. The bus waveguide is pumped at one of the ring resonances with the minimum dispersion (1543.5 nm in our case) to satisfy the principle of energy conservation. The photon pair generation rate achieved is comparable to the state of the art. The photon pair sources exploiting nonlinear frequency conversion/generation processes is a promising alternative to atom-like single-photon emitters in the field of integrated photonics. Such miniaturized structures will benefit future on-chip architectures where multiple single-photon source devices are required on the same chip.

Physical Sciences Forum doi: 10.3390/psf2024010001

Authors: Ben Sicks Florian Maiss Christian Lingenfelder Cornelia Wiegand Martin Hessling

The recently much noticed Far-UVC spectral range offers the possibility of inactivating pathogens without necessarily posing a major danger to humans. Unfortunately, there are various Far-UVC sources that differ significantly in their longer wavelength UVC emission and, subsequently, in their risk potential. Therefore, a simple assessment method for Far-UVC sources is presented here. In addition, the temporal intensity stability of Far-UVC sources was examined in order to reduce possible errors in irradiation measurements. For this purpose, four Far-UVC sources and a conventional Hg UVC lamp were each spectrally measured for about 100 h and mathematically evaluated for their antimicrobial effect and hazard potential using available standard data. The two filtered KrCl lamps were found to be most stable after a warm-up time of 30 min. With regard to the antimicrobial effect, the radiation efficiencies of all examined (Far-) UVC sources were more or less similar. However, the calculated differences in the potential human hazard to eyes and skin were more than one order of magnitude. The two filtered KrCl lamps were the safest, followed by an unfiltered KrCl lamp, a Far-UVC LED and, finally, the Hg lamp. When experimenting with these Far-UVC radiation sources, the irradiance should be checked more than once. If UVC radiation is to be or could be applied in the presence of humans, filtered KrCl lamps are a much better choice than any other available Far-UVC sources.

Physical Sciences Forum doi: 10.3390/psf2023009026

Authors: Ning Xiang Tomislav Jasa

Since its inception in 2004, nested sampling has been used in acoustics applications. This work applies nested sampling within a Bayesian framework to the detection and localization of sound sources using a spherical microphone array. Beyond an existing work, this source localization task relies on spherical harmonics to establish parametric models that distinguish the background sound environment from the presence of sound sources. Upon a positive detection, the parametric models are also involved to estimate an unknown number of potentially multiple sound sources. For the purpose of source detection, a no-source scenario needs to be considered in addition to the presence of at least one sound source. Specifically, the spherical microphone array senses the sound environment. The acoustic data are analyzed via spherical Fourier transforms using a Bayesian model comparison of two different models accounting for the absence and presence of sound sources for the source detection. Upon a positive detection, potentially multiple source models are involved to analyze direction of arrivals (DoAs) using Bayesian model selection and parameter estimation for the sound source enumeration and localization. These are two levels (enumeration and localization) of inferential estimations necessary to correctly localize potentially multiple sound sources. This paper discusses an efficient implementation of the nested sampling algorithm applied to the sound source detection and localization within the Bayesian framework.

Physical Sciences Forum doi: 10.3390/psf2023009025

Authors: Guillaume Lebonvallet Faicel Hnaien Hichem Snoussi

This work introduces a new method for the exploration of solutions space in complex problems. This method consists of the build of a latent space which gives a new encoding of the solution space. We map the objective function on the latent space using a manifold, i.e., a mathematical object defined by an equations system. The latent space is built with some knowledge of the objective function to make the mapping of the manifold easier. In this work, we introduce a new encoding for the Travelling Salesman Problem (TSP) and we give a new method for finding the optimal round.

Physical Sciences Forum doi: 10.3390/psf2023008073

Authors: Yiding Yu Thomas Joseph Carroll Sudeshna Ganguly Karol Lang Eduardo Ossorio Pavel Snopok Jennifer Thomas Don Athula Wickremasinghe Katsuya Yonehara

Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis νμ Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the Main Injector (NuMI) beam. This study endeavors to ensure the monitor signal quality and to correlate them with the Neutrino beam profile. Leveraging muon monitor simulations, we systematically explore the monitor responses to variations in proton-beam and lattice parameters. Through the amalgamation of individual pixel data from muon monitors, pattern-recognition algorithms, simulations, and measured data, we devise machine-learning-based models to predict muon monitor responses and Neutrino flux.

Physical Sciences Forum doi: 10.3390/psf2023009024

Authors: Valérie Girardin Théo Grente Nathalie Niquil Philippe Regnault

In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar feeding behaviors and metabolisms and with the same predators. To take into account the unknown flow values within food webs, a class of methods called Linear Inverse Modeling was developed. The total linear constraints, equations and inequations defines a multidimensional convex-bounded polyhedron, called a polytope, within which lie all realistic solutions to the problem. To describe this polytope, a possible method is to calculate a representative sample of solutions by using the Monte Carlo Markov Chain approach. In order to extract a unique solution from the simulated sample, several goal (cost) functions—also called Ecological Network Analysis indices—have been introduced in the literature as criteria of fitness to the ecosystems. These tools are all related to information theory. Here we introduce new functions that potentially provide a better fit of the estimated model to the ecosystem.

Physical Sciences Forum doi: 10.3390/psf2023008072

Authors: Lucile Mellet Mathieu Guigue Boris Popov Stefano Russo Vincent Voisin

The construction of the next-generation water Cherenkov detector Hyper-Kamiokande (HK) has started. It will have about a ten times larger fiducial volume compared to the existing Super-Kamiokande detector, as well as increased detection performances. The data collection process is planned from 2027 onwards. Time stability is crucial, as detecting physics events relies on reconstructing Cherenkov rings based on the coincidence between the photomultipliers. The above requires a distributed clock jitter at each endpoint that is smaller than 100 ps. In addition, since this detector will be mainly used to detect neutrinos produced by the J-PARC accelerator in Tokai, each event needs to be timed-tagged with a precision better than 100 ns, with respect to UTC, in order to be associated with a proton spill from J-PARC or the events observed in other detectors for multi-messenger astronomy. The HK collaboration is in an R&D phase and several groups are working in parallel for the electronics system. This proceeding will present the studies performed at LPNHE (Paris) related to a novel design for the time synchronization system in Kamioka with respect to the previous KamiokaNDE series of experiments. We will discuss the clock generation, including the connection scheme between the GNSS receiver (Septentrio) and the atomic clock (free-running Rubidium), the precise calibration of the atomic clock and algorithms to account for errors on satellites orbits, the redundancy of the system, and a two-stage distribution system that sends the clock and various timing-sensitive information to each front-end electronics module, using a custom protocol.

Physical Sciences Forum doi: 10.3390/psf2023009023

Authors: Minas Karamanis Uroš Seljak

We present preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference in complex probability distributions. PMC incorporates a normalizing flow (NF) and an adaptive Sequential Monte Carlo (SMC) scheme, along with a novel past resampling scheme to boost the number of propagated particles without extra computational costs. Additionally, we utilize preconditioned Crank–Nicolson updates, enabling PMC to scale to higher dimensions without the gradient of target distribution. The efficacy of PMC in producing samples, estimating model evidence, and executing robust inference is showcased through two challenging case studies, highlighting its superior performance compared to conventional methods.

Physical Sciences Forum doi: 10.3390/psf2023009022

Authors: John Skilling

We seek to add up Q=∫fdX over unit volume in arbitrary dimension. Nested sampling locates the bulk of Q by geometrical compression, using a Monte Carlo ensemble constrained within a progressively more restrictive lower limit f≤f*. This domain is divided into a core f>f* and a shell f=f*, with the core kept adequately populated.

Physical Sciences Forum doi: 10.3390/psf2023009021

Authors: Richard D. P. Grumitt Minas Karamanis Uroš Seljak

For many scientific inverse problems, we are required to evaluate an expensive forward model. Moreover, the model is often given in such a form that it is unrealistic to access its gradients. In such a scenario, standard Markov Chain Monte Carlo algorithms quickly become impractical, requiring a large number of serial model evaluations to converge on the target distribution. In this paper, we introduce Flow Annealed Kalman Inversion (FAKI). This is a generalization of Ensemble Kalman Inversion (EKI) where we embed the Kalman filter updates in a temperature annealing scheme and use normalizing flows (NFs) to map the intermediate measures corresponding to each temperature level to the standard Gaussian. Thus, we relax the Gaussian ansatz for the intermediate measures used in standard EKI, allowing us to achieve higher-fidelity approximations to non-Gaussian targets. We demonstrate the performance of FAKI on two numerical benchmarks, showing dramatic improvements over standard EKI in terms of accuracy whilst accelerating its already rapid convergence properties (typically in O(10) steps).

Physical Sciences Forum doi: 10.3390/psf2023009020

Authors: Mats Leif Moskopp Andreas Deussen Peter Dieterich

The analysis and evaluation of microscopic image data is essential in life sciences. Increasing temporal and spatial digital image resolution and the size of data sets promotes the necessity of automated image analysis. Previously, our group proposed a Bayesian formalism that allows for converting the experimenter’s knowledge, in the form of a manually segmented image, into machine-readable probability distributions of the parameters of an image segmentation pipeline. This approach preserved the level of detail provided by expert knowledge and interobserver variability and has proven robust to a variety of recording qualities and imaging artifacts. In the present work, Bayesian evidences were used to compare different image processing pipelines. As an illustrative example, a microscopic phase contrast image of a wound healing assay and its manual segmentation by the experimenter (ground truth) are used. Six different variations of image segmentation pipelines are introduced. The aim was to find the image segmentation pipeline that is best to automatically segment the input image given the expert knowledge with respect to the principle of Occam’s razor to avoid unnecessary complexity and computation. While none of the introduced image segmentation pipelines fail completely, it is illustrated that assessing the quality of the image segmentation with the naked eye is not feasible. Bayesian evidence (and the intrinsically estimated uncertainty σ of the image segmentation) is used to choose the best image processing pipeline for the given image. This work illustrates a proof of principle and is extendable to a diverse range of image segmentation problems.

Physical Sciences Forum doi: 10.3390/psf2023009019

Authors: Margret Westerkamp Jakob Roth Philipp Frank Will Handley Torsten Enßlin

Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which can hamper high-accuracy determinations of the evidence values as estimated from the likelihood-prior-volume function. We introduce an approach based on information field theory, a framework for non-parametric function reconstruction from data, that infers the likelihood-prior-volume function by exploiting its smoothness and thereby aims to improve the evidence calculation. Our method provides posterior samples of the likelihood-prior-volume function that translate into a quantification of the remaining sampling noise for the evidence estimate, or for any other quantity derived from the likelihood-prior-volume function.

Physical Sciences Forum doi: 10.3390/psf2023009018

Authors: Fabrizia Guglielmetti Michele Delli Veneri Ivano Baronchelli Carmen Blanco Andrea Dosi Torsten Enßlin Vishal Johnson Giuseppe Longo Jakob Roth Felix Stoehr Łukasz Tychoniec Eric Villard

An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA-calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and the overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and testing new algorithms.

Physical Sciences Forum doi: 10.3390/psf2023009017

Authors: Johannes Buchner

A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. This stabilizes the MCMC proposal of later MCMC proposals, and leads to pleasant properties, including that the number of live points and number of MCMC steps do not have to be calibrated, that the evidence and posterior approximation improve as more compute is added and can be diagnosed with convergence diagnostics from the MCMC community. Snowballing nested sampling converges to a “perfect” nested sampling run with an infinite number of MCMC steps.

Physical Sciences Forum doi: 10.3390/psf2023009016

Authors: Vishal Johnson Philipp Frank Torsten Enßlin

We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue that correlation is a resource that is consumed when measurements take place. It is also argued that a network of such measurements establishes a stable objective classical reality.

Physical Sciences Forum doi: 10.3390/psf2023008071

Authors: Gilles Ferrand

Visualization is an essential part of research, both to explore one’s data and to communicate one’s findings with others. Many data products in astronomy come in the form of multi-dimensional cubes, and since our brains are tuned for recognition in a 3D world, we ought to display and manipulate these in 3D space. This is possible with virtual reality (VR) devices. Drawing from our experiments developing immersive and interactive 3D experiences from actual science data at the Astrophysical Big Bang Laboratory (ABBL), this paper gives an overview of the opportunities and challenges that are awaiting astrophysicists in the burgeoning VR space. It covers both software and hardware matters, as well as practical aspects for successful delivery to the public.

Physical Sciences Forum doi: 10.3390/psf2023008070

Authors: Minjin Jeong

Although there is overwhelming evidence for the existence of dark matter, the nature of dark matter remains largely unknown. Neutrino telescopes are powerful tools to search indirectly for dark matter, through the detection of neutrinos produced during dark matter decay or annihilation processes. The IceCube Neutrino Observatory is a cubic-kilometer-scale neutrino telescope located under 1.5 km of ice near the Amundsen-Scott South Pole Station. Various dark matter searches were performed with IceCube over the last decade, providing strong constraints on dark matter models. In this contribution, we present the latest results from IceCube as well as ongoing analyses using IceCube data, focusing on the works that look at the Galactic Halo, nearby galaxies, and galaxy clusters.

Physical Sciences Forum doi: 10.3390/psf2023009015

Authors: Sascha Ranftl Shaoheng Guan

It has previously been shown that prior physics knowledge can be incorporated into the structure of an artificial neural network via neural activation functions based on (i) the correspondence under the infinite-width limit between neural networks and Gaussian processes if the central limit theorem holds and (ii) the construction of physics-consistent Gaussian process kernels, i.e., specialized covariance functions that ensure that the Gaussian process fulfills a priori some linear (differential) equation. Such regression models can be useful in many-query problems, e.g., inverse problems, uncertainty quantification or optimization, when a single forward solution or likelihood evaluation is costly. Based on a small set of training data, the learned model or “surrogate” can then be used as a fast approximator. The bottleneck is then for the surrogate to also learn efficiently and effectively from small data sets while at the same time ensuring physically consistent predictions. Based on this, we will further explore the properties of so-constructed neural networks. In particular, we will characterize (i) generalization behavior and (ii) the approximation quality or Gaussianity as a function of network width and discuss (iii) extensions from shallow to deep NNs.

Physical Sciences Forum doi: 10.3390/psf2023009014

Authors: Ali Mohammad-Djafari Ning Chu Li Wang Liang Yu

Inverse problems arise anywhere we have an indirect measurement. In general, they are ill-posed to obtain satisfactory solutions, which needs prior knowledge. Classically, different regularization methods and Bayesian inference-based methods have been proposed. As these methods need a great number of forward and backward computations, they become costly in computation, particularly when the forward or generative models are complex, and the evaluation of the likelihood becomes very costly. Using deep neural network surrogate models and approximate computation can become very helpful. However, in accounting for the uncertainties, we need first to understand Bayesian deep learning, and then we can see how we can use it for inverse problems. In this work, we focus on NN, DL, and, more specifically, the Bayesian DL particularly adapted for inverse problems. We first give details of Bayesian DL approximate computations with exponential families; then, we see how we can use them for inverse problems. We consider two cases: First, we consider the case where the forward operator is known and used as a physics constraint, and the second examines more general data-driven DL methods.

Physical Sciences Forum doi: 10.3390/psf2023009013

Authors: Jason D. McEwen Tobías I. Liaudat Matthew A. Price Xiaohao Cai Marcelo Pereyra

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. First, we review proximal nested sampling in a pedagogical manner in an attempt to elucidate the framework for physical scientists. Second, we show how proximal nested sampling can be extended in an empirical Bayes setting to support data-driven priors, such as deep neural networks learned from training data.

Physical Sciences Forum doi: 10.3390/psf2023009011

Authors: Anselm Hohlstamm Andreas Deussen Stephan Speier Peter Dieterich

Endothelial cells keep a tight and adaptive inner cell layer in blood vessels. Thereby, the cells develop complex dynamics through integrating active individual and collective cell migration, cell-cell interactions as well as interactions with external stimuli. It is the aim of this study to quantify and model these underlying dynamics. Therefore, we seeded and stained human umbilical vein endothelial cells (HUVECs) and recorded their positions every 10 min for 48 h via live-cell imaging. After image segmentation and tracking of several 10.000 cells, we applied Bayesian data analysis to models assessing the experimentally obtained cell trajectories. By analyzing the mean squared velocities, we found a dependence on the local cell density. Based on this connection, we developed a model, which approximates the time-dependent frequency of cell divisions. Furthermore, we determined two different phases of velocity deceleration, which are influenced by the emergence of correlated cell movements and time-dependent aging in this non-stationary system. By integrating the findings of correlation functions, we will be able to develop a comprehensive model to improve the understanding of endothelial cell migration in the future.

Physical Sciences Forum doi: 10.3390/psf2023009012

Authors: Seyedeh Azadeh Fallah Mortezanejad Ali Mohammad-Djafari

Variational Bayesian Approximation (VBA) is a fast technique for approximating Bayesian computation. The main idea is to assess the joint posterior distribution of all the unknown variables with a simple expression. Mean–Field Variational Bayesian Approximation (MFVBA) is a particular case developed for large–scale problems where the approximated probability law is separable in all variables. A well–known drawback of MFVBA is that it tends to underestimate the variances in the variables, even though it estimates the means well. It can lead to poor inference results. We can obtain a fixed point algorithm to evaluate the means in exponential families for the approximating distribution. However, this does not solve the problem of underestimating the variances. In this paper, we propose a modified method of VBA with exponential families to first estimate the posterior mean and then improve the estimation of the posterior covariance. We demonstrate the performance of the procedure with an example.

Physical Sciences Forum doi: 10.3390/psf2023009010

Authors: Alicja Polanska Matthew A. Price Alessio Spurio Mancini Jason D. McEwen

Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare models. The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood. The learned harmonic mean estimator learns an importance sampling target distribution that approximates the optimal distribution. While the approximation need not be highly accurate, it is critical that the probability mass of the learned distribution is contained within the posterior in order to avoid the exploding variance problem. In previous work, a bespoke optimization problem is introduced when training models in order to ensure this property is satisfied. In the current article, we introduce the use of normalizing flows to represent the importance sampling target distribution. A flow-based model is trained on samples from the posterior by maximum likelihood estimation. Then, the probability density of the flow is concentrated by lowering the variance of the base distribution, i.e., by lowering its “temperature”, ensuring that its probability mass is contained within the posterior. This approach avoids the need for a bespoke optimization problem and careful fine tuning of parameters, resulting in a more robust method. Moreover, the use of normalizing flows has the potential to scale to high dimensional settings. We present preliminary experiments demonstrating the effectiveness of the use of flows for the learned harmonic mean estimator. The harmonic code implementing the learned harmonic mean, which is publicly available, has been updated to now support normalizing flows.

Physical Sciences Forum doi: 10.3390/psf2023009009

Authors: Danielle Hodgkinson Joel Fajans Jonathan S. Wurtele

The ALPHA-g experiment at CERN intends to observe the effect of gravity on antihydrogen. In ALPHA-g, antihydrogen is confined to a magnetic trap with an axis aligned parallel to the Earth’s gravitational field. An imposed difference in the magnetic field of the confining coils above and below the trapping region, known as a bias, can be delicately adjusted to compensate for the gravitational potential experienced by the trapped anti-atoms. With the bias maintained, the magnetic fields of the coils can be ramped down slowly compared to the anti-atom motion; this releases the antihydrogen and leads to annihilations on the walls of the apparatus, which are detected by a position-sensitive detector. If the bias cancels out the gravitational potential, antihydrogen will escape the trap upwards or downwards with equal probability. Determining the downward (or upward) escape probability, p, from observed annihilations is non-trivial because the annihilation detection efficiency may be up–down asymmetric; some small fraction of antihydrogen escaping downwards may be detected in the upper region (and vice versa) meaning that the precise number of trapped antihydrogen atoms is unknown. In addition, cosmic rays passing through the apparatus lead to a background annihilation rate, which may also be up–down asymmetric. We present a Bayesian method to determine p by assuming annihilations detected in the upper and lower regions are independently Poisson distributed, with the Poisson mean expressed in terms of experimental quantities. We solve for the posterior p using the Markov chain Monte Carlo integration package, Stan. Further, we present a method to determine the gravitational acceleration of antihydrogen, ag, by modifying the analysis described above to include simulation results. In the modified analysis, p is replaced by the simulated probability of downward escape, which is a function of ag.

Physical Sciences Forum doi: 10.3390/psf2023009008

Authors: Franziska Zimmermann Frauke Viola Härtel Anupam Das Thomas Noll Peter Dieterich

Endothelial barrier function can be quantified by the determination of the transendothelial resistance (TER) via impedance spectroscopy. However, TER can only be obtained indirectly based on a mathematical model. Models usually comprise a sequence of a resistance in parallel with a capacitor (RC-circuit), each for the cell layer (including TER) and the filter substrate, one resistance (R) for the medium, and a constant phase element (CPE) for the electrode–electrolyte interface. We applied Bayesian data analysis on a variety of model variants. Phase and absolute values of impedance data were acquired over time by a commercial device for measurements of pure medium, medium and raw filter, and medium with cell-covered filter stimulated with different agents. Medium and raw filter were best described by a series of four and three RC-circuits, respectively. Parameter estimation of the TER showed a concentration-dependent decrease in response to thrombin. Model comparison indicated that even high concentrations of thrombin did not fully disrupt the endothelial barrier. Insights in the biophysical meaning of model parameters were gained through complemental cell-free measurements with sodium chloride. In summary, Bayesian analysis allows for valid parameter estimation and the selection of models with different complexity under various experimental conditions to characterize endothelial barrier function.

Physical Sciences Forum doi: 10.3390/psf2023008069

Authors: Tord Ekelöf

A design study ESSνSB was carried out during the years 2018–2021 concerning how the five MW linear proton accelerators of the European Spallation Source, which are currently under construction in Lund, Sweden, can be used to generate a world-unique, intense neutrino Super Beam for precision measurements of the leptonic CP violating phase δCP. As there are definite limits, which are related to uncertainties in neutrino–nucleus interaction modeling, to how far the systematic errors in such measurements can be reduced, the method chosen in this project is to make the measurements at the second oscillation maximum, where the CP violation signal is close to three times larger than at the first, whereas the systematic errors are approximately the same at the two maxima. As the second maximum is located three times further away from the neutrino source than the first maximum, a higher neutrino beam intensity and thus a higher proton driver power are required when measuring at the second maximum. The unique high power of the ESS proton linac will allow for the measurements to be made at the second maximum and thereby for the most precise measurements of the leptonic CP violation phase δCP to be made. This paper describes the results of the work made on the conceptual design of ESSνSB layout, infrastructure, and components as well as the evaluation of the physics performance for leptonic CP violation discovery and, in particular, the precision in the measurement of δCP.

Physical Sciences Forum doi: 10.3390/psf2023009007

Authors: Risinie D. Perera Kevin H. Knuth

Assessing the habitability of exoplanets (planets orbiting other stars) is of great importance in deciding which planets warrant further careful study. Planets in the habitable zones of stars like our Sun are sufficiently far away from the star so that the light rays from the star can be assumed to be parallel, leading to straightforward analytic models for stellar illumination of the planet’s surface. However, for planets in the close-in habitable zones of dim red dwarf stars, such as the potentially habitable planet orbiting our nearest stellar neighbor, Proxima Centauri, the analytic illumination models based on the parallel ray approximation do not hold, resulting in severe biases in the estimates of the planetary characteristics, thus impacting efforts to understand the planet’s atmosphere and climate. In this paper, we present our efforts to improve the instellation (stellar illumination) models for close-in orbiting planets and the significance of the implementation of these improved models into EXONEST, which is a Bayesian machine learning application for exoplanet characterization. The ultimate goal is to use these improved models and parameter estimates to model the climates of close-in orbiting exoplanets using planetary General Circulation Models (GCM). More specifically, we are working to apply our instellation corrections to the NASA ROCKE-3D GCM to study the climates of the potentially habitable planets in the Trappist-1 system.

Physical Sciences Forum doi: 10.3390/psf2023009006

Authors: Robert Köberl Robert Babin Christopher G. Albert

We report on progress towards a probabilistic framework for consistent uncertainty quantification and propagation in the analysis and numerical modeling of physics in magnetically confined plasmas in the stellarator configuration. A frequent starting point in this process is the calculation of a magnetohydrodynamic equilibrium from plasma profiles. Profiles, and thus the equilibrium, are typically reconstructed from experimental data. What sets equilibrium reconstruction apart from usual inverse problems is that profiles are given as functions over a magnetic flux derived from the magnetic field, rather than spatial coordinates. This makes it a fixed-point problem that is traditionally left inconsistent or solved iteratively in a least-squares sense. The aim here is progressing towards a straightforward and transparent process to quantify and propagate uncertainties and their correlations for function-valued fields and profiles in this setting. We propose a framework that utilizes a low-dimensional prior distribution of equilibria, constructed with principal component analysis. A surrogate of the forward model is trained to enable faster sampling.

Physical Sciences Forum doi: 10.3390/psf2023009005

Authors: Geert Verdoolaege

Geodesic least squares (GLS) is a regression technique that operates in spaces of probability distributions. Based on the minimization of the Rao geodesic distance between two probability models of the response variable, GLS is robust against outliers and model misspecification. The method is very simple, without any tuning parameters, owing to its solid foundations rooted in information geometry. Here, we illustrate the robustness properties of GLS using applications in the fields of magnetic confinement fusion and astrophysics. Additional interpretation is gained from visualizations using several models for the manifold of Gaussian probability distributions.

Physical Sciences Forum doi: 10.3390/psf2023009004

Authors: David Iagaru Nina Maria Gottschling

The recently deployed Sentinel 2 satellite constellation produces images in 13 wavelength bands with a Ground Sampling Distance (GSD) of 10 m, 20 m, and 60 m. Super-resolution aims to generate all 13 bands with a spatial resolution of 10 m. This paper investigates the performance of DSen2, a proposed convolutional neural network (CNN)-based method, for tackling super-resolution in terms of accuracy and uncertainty. As the optimization problem for obtaining the weights of a CNN is highly non-convex, there are multiple different local minima for the loss function. This results in several possible CNN models with different weights and thus implies epistemic uncertainty. In this work, methods to quantify epistemic uncertainty, termed weighted deep ensembles (WDESen2) and its variants), are proposed. These allow the quantification of predictive uncertainty estimates and, moreover, the improvement of the accuracy of the prediction by selective prediction. They involve a consideration of deep ensembles, and each model’s importance can be weighted depending on the model’s validation loss. We show that weighted deep ensembles improve the accuracy of prediction compared to state-of-the-art methods and deep ensembles. Moreover, the uncertainties can be linked to the underlying inverse problem and physical patterns on the ground. This allows us to improve the trustworthiness of CNN predictions and the predictive accuracy with selective prediction.

Physical Sciences Forum doi: 10.3390/psf2023009003

Authors: Fabian Sigler Viktoria Kainz Torsten Enßlin Céline Boehm Sonja Utz

Humans make decisions about their actions based on a combination of their objectives and their knowledge about the state of the world surrounding them. In social interactions, one prevalent goal is the ambition to be perceived to be an honest, trustworthy person in terms of having a reputation of frequently making true statements. Aiming for this goal requires the decision whether to communicate information truthfully or if deceptive lies might improve the reputation even more. The basis of this decision involves not only an individual’s belief about others, but also their understanding of others’ beliefs, described by the concept of Theory of Mind, and the mental processes from which these beliefs emerge. In the present work, we used the Reputation Game Simulation as an approach for modeling the evolution of reputation in agent-based social communication networks, in which agents treat information approximately according to Bayesian logic. We implemented a second-order Theory of Mind based message decision strategy that allows the agents to mentally simulate the impact of different communication options on the knowledge of their counterparts’ minds in order to identify the message that is expected to maximize their reputation. Analysis of the communication patterns obtained showed that deception was chosen more frequently than the truthful communication option. However, the efficacy of such deceptive behavior turned out to have a strong correlation with the accuracy of the agents’ Theory of Mind representation.

Physical Sciences Forum doi: 10.3390/psf2023009002

Authors: Nhan Le Hichem Snoussi Alain Iltis

Conventional maximum likelihood-based algorithms for 3D Compton image reconstruction are often stuck with slow convergence and large data volume, which could be unsuitable for some practical applications, such as nuclear engineering. Taking advantage of the Bayesian framework, we propose a fast-converging iterative maximum a posteriori reconstruction algorithm under the assumption of the Poisson data model and Markov random field-based convex prior in this paper. The main originality resides in developing a new iterative maximization scheme with simultaneous updates following the line search strategy to bypass the spatial dependencies among neighboring voxels. Numerical experiments on real datasets conducted with hand-held Temporal Compton cameras developed by Damavan Imaging company and punctual 0.2 MBq 22Na sources with zero-mean Gaussian Markov random field confirm the outperformance of the proposed maximum a posteriori algorithm over various existing expectation–maximization type solutions.

Physical Sciences Forum doi: 10.3390/psf2023009001

Authors: Udo von Toussaint Roland Preuss

The forty-second International Conference on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (42nd MaxEnt’23) was held at the Max Planck Institute for Plasmaphysics (IPP) in Garching, Germany, from 3rd to 7th of July 2023 (https://www [...]

Physical Sciences Forum doi: 10.3390/psf2023008068

Authors: Supraja Balasubramanian

SBND (Short-Baseline Near Detector) is a 112-ton liquid argon time projection chamber located on the Booster Neutrino Beam at Fermi National Accelerator Laboratory, and is the near detector of the Short-Baseline Neutrino program. The primary goals of SBND are to provide flux constraints for sterile neutrino searches, conduct world-leading neutrino cross-section measurements on argon, and perform Beyond the Standard Model (BSM) new physics searches with its high-precision particle identification capabilities. SBND’s prospects and tools for detecting a variety of BSM phenomena produced in a neutrino beam, such as sub-GeV dark matter, dark neutrinos, heavy neutral leptons and millicharged particles, are discussed.

Physical Sciences Forum doi: 10.3390/psf2023008067

Authors: Marcos Dracos

The European project ESSνSB, after a four-year feasibility study, has demonstrated that a neutrino facility based on the European Spallation Source and operated at the second oscillation maximum is not only compatible with the under construction neutron facility, but it also has a very high physics performance in the sector of discovery of CP violation in the leptonic sector and measurement of the CP-violating phase with high precision. This has been obtained by well optimising all parts of this neutrino facility going from the ESS proton linac up to the location of the neutrino far detector. Here, a summary of all these efforts based on the already published Conceptual Design Report is reported. A continuation of this work has recently been approved by EU. This new project includes investigations of implementation of low energy nuSTORM and ENUBET for cross-section measurements and sterile neutrino searches. Both options use mainly muons produced together with neutrinos. This “muon” orientation gives a new dimension to the project, enhancing its probability to be approved in the future.

Physical Sciences Forum doi: 10.3390/psf2023008066

Authors: Dibya S. Chattopadhyay Kaustav Chakraborty Amol Dighe Srubabati Goswami

We present compact analytic expressions for neutrino propagation probabilities in matter, with effects from the invisible decay of the ν3 mass eigenstate included. These will be directly relevant for long-baseline experiments. The inclusion of decay leads to a non-Hermitian effective Hamiltonian, with the Hermitian part corresponding to oscillation, and the anti-Hermitian part representing the decay. In the presence of matter, the two components invariably become non-commuting. We employ the Cayley–Hamilton theorem to calculate the neutrino oscillation probabilities in constant density matter. The analytic results obtained provide a physical understanding of the possible effects of neutrino decay on these probabilities. Certain non-intuitive features like an increase in the survival probability P(νμ→νμ) at its oscillation dips may be explained using our analytic expressions.

Physical Sciences Forum doi: 10.3390/psf2023008065

Authors: E. G. Parozzi F. Acerbi I. Angelis L. Bomben M. Bonesini F. Bramati A. Branca C. Brizzolari G. Brunetti M. Calviani S. Carturan M. G. Catanesi S. Cecchini N. Charitonidis F. Cindolo G. Cogo G. Collazuol F. Dal Corso C. Delogu G. De Rosa A. Falcone B. Goddard A. Gola L. Halić F. Iacob C. Jollet V. Kain A. Kallitsopoulou B. Klicek Y. Kudenko C. Lampoudis M. Laveder P. Legou A. Longhin L. Ludovici E. Lutsenko L. Magaletti G. Mandrioli S. Marangoni A. Margotti V. Mascagna N. Mauri L. Meazza A. Meregaglia M. Mezzetto M. Nessi A. Paoloni M. Pari T. Papaevangelou L. Pasqualini G. Paternoster L. Patrizii M. Pozzato M. Prest F. Pupilli E. Radicioni A. C. Ruggeri D. Sampsonidis C. Scian G. Sirri M. Stipcevic M. Tenti F. Terranova M. Torti S. E. Tzamarias E. Vallazza F. Velotti L. Votano

The ENUBET project aims to reduce the flux-related systematics to 1% on a narrow band neutrino beam through monitoring the associated charged leptons in an instrumented decay tunnel. A key element of the project is the design of a meson transfer line with conventional magnets that maximize the yield of K+ and π+ while minimizing the total length to reduce meson decay outside the instrumented region. In order to limit particle rates in the tunnel instrumentation, a high level of beam collimation is needed, thus allowing non-decayed mesons to reach the end of the tunnel. At the same time, fine-tuning of the shielding and the collimators is required to minimize any beam-induced background in the decay region. The magnetic lattice is optimized with TRANSPORT. The focusing of mesons from the target is performed with a static (quadrupole-based) system that, coupled with a slow proton extraction scheme, allows for a significant pile-up reduction at the tunnel instrumentation while retaining a particle yield large enough for high-precision neutrino cross-section measurements on a 3 year time scale. Charge and momentum selection in an 8.5GeV ± 10% momentum bite is performed by a double dipole system. Shielding elements are optimized with full simulation of the facility in Geant4. In particular, a powerful genetic algorithm is used to scan the parameter space of the collimators automatically in order to find a configuration that minimizes the halo background in the decay tunnel while preserving a large meson yield. This contribution will report the results of the optimization studies and the final design of the ENUBET beamline, together with dose estimation through a FLUKA simulation. The design of an alternative secondary beamline with a broad momentum range (4, 6, and 8.5 GeV/c) that could enhance the physics reach of the facility is additionally discussed.

Physical Sciences Forum doi: 10.3390/psf2023008064

Authors: Barbara Yaeggy

DUNE’s Argon time-projecting chambers (TPC) detectors will allow us to conduct precise studies about phenomena that have, until now, seemed too challenging to measure, like tau neutrino (ντ) interactions. Cross section measurements are needed to understand how accurate our neutrino-nucleus interaction models are and how accurately we can use them to reconstruct neutrino energy. Quasi-elastic scattering (QE), Δ resonance production (RES), and deep inelastic scattering (DIS) processes are known to provide dominant contributions in the medium and high neutrino energy to the total cross-section of ντ(N) and ν¯τ(N). These cross-sections have large systematic uncertainties compared to the ones measured for νμ and νe and their antiparticles. Studies point out that the reason for these differences is due to the model dependence of the ντ(N) cross-sections in treating the nuclear medium effects described by the nucleon structure functions, F1N,⋯,3N(x,Q2) for νμ and νe. These proceedings show the semi-theoretical and experimental approach to the estimation of the ντ(N) and ν¯τ(N) cross-sections in DUNE for the DIS region. We will check the contributions of the additional nucleon structure functions F4N(x,Q2) and F5N(x,Q2) and their dependence on Q2 and Bjorken-x scale.

Physical Sciences Forum doi: 10.3390/psf2023006009

Authors: Mebarka Daoudi Syham Kadri Youcef Himri Mohamed Bensafi Abdelkarim Talhi

The University of Bechar, Algeria, held an international conference on physics, renewable energies, and environment (ICPSDREE) from 14 to 16 November 2022 [...]

Physical Sciences Forum doi: 10.3390/psf2023006008

Authors: Mebarka Daoudi Syham Kadri Youcef Himri Mohamed Bensafi Abdelkarim Talhi

In submitting conference proceedings to Physical Sciences Forum, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Physical Sciences Forum doi: 10.3390/psf2023008063

Authors: Nicholas Prouse Patrick de Perio Wojciech Fedorko

Hyper-Kamiokande (Hyper-K) is the next-generation water Cherenkov neutrino experiment, building on the success of its predecessor Super-Kamiokande. To match the increased precision and reduced statistical errors of the new detectors, improvements to event reconstruction and event selection are required to suppress backgrounds and minimise systematic errors. Machine learning has the potential to provide these enhancements, enabling the precision measurements that Hyper-K aims to perform. This paper provides an overview of the areas where machine learning is being explored for Hyper-K’s water Cherenkov detectors. Results using various network architectures are presented, along with comparisons to traditional methods and a discussion of the challenges and future plans for applying machine learning techniques.

Physical Sciences Forum doi: 10.3390/psf2023008061

Authors: Khushboo Dixit Akhila Kumar Pradhan S. Uma Sankar

The Majorana phases of neutrino mixing matrix do not appear either in vacuum or in matter modified oscillation probabilities. It was previously shown that for some particular forms of decoherence, the neutrino oscillations do depend on Majorana phases. Here, we show that such dependence also occurs for neutrino decay scenarios where mass eigenstates are not the decay eigenstates. We calculate two flavour survival/oscillation probabilities in such a scenario and discuss their CP and CPT properties.

Physical Sciences Forum doi: 10.3390/psf2023008062

Authors: Shiqi Yu

The IceCube Neutrino Observatory is a Cherenkov detector located at the South Pole, instrumenting a cubic kilometer of ice. The DeepCore subdetector is located at the lower center of the IceCube array, and has denser configuration that has improved ability to see GeV-scale neutrinos in the detector. Convolutional neural networks (CNN) are used to reconstruct neutrino interactions in DeepCore, achieving comparable performance to the current likelihood-based method but with roughly 3000 times faster processing speeds. In this study, we present a preliminary atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample, and the sensitivity to neutrino oscillation parameter measurements is shown and compared to the recent IceCube results.