Physical Sciences Forum

60 pages, 476 KB

Open AccessConference Report

Abstracts of the 3rd International Online Conference on Universe, 4–6 March 2026

by Lorenzo Iorio

Phys. Sci. Forum 2026, 14(1), 1; https://doi.org/10.3390/psf2026014001 - 18 May 2026

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, [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Universe)

10 pages, 420 KB

Open AccessProceeding Paper

Atomic Structure Analysis and Radiative Properties with Einstein Coefficients for Ne-like Se (Se XXV)

by Malvika Singh, Richa Paijwar and Rinku Sharma

Phys. Sci. Forum 2026, 13(1), 6; https://doi.org/10.3390/psf2026013006 - 13 May 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Atoms)

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8 pages, 276 KB

Open AccessProceeding Paper

Resonant Excitation in r-Process Collision Strengths for Non-LTE Kilonova Modelling

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

Phys. Sci. Forum 2026, 13(1), 5; https://doi.org/10.3390/psf2026013005 (registering DOI) - 11 May 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Atoms)

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9 pages, 7600 KB

Open AccessProceeding Paper

Electron-Impact Single Ionization of Molecules: Orientation-Resolved Fully Differential Cross Sections

by Emiliano Acebal and Sebastian Otranto

Phys. Sci. Forum 2026, 13(1), 2; https://doi.org/10.3390/psf2026013002 - 30 Apr 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Atoms)

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7 pages, 7513 KB

Open AccessProceeding Paper

State-Selective Charge Exchange in Collisions of Multiply Charged Ions with H₂

by Nelson D. Cariatore and Sebastian Otranto

Phys. Sci. Forum 2026, 13(1), 4; https://doi.org/10.3390/psf2026013004 (registering DOI) - 28 Apr 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Atoms)

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8 pages, 1271 KB

Open AccessProceeding Paper

Atomic Energy Level Calculations for Lanthanides with AUTOSTRUCTURE

by Tomás Campante, Ricardo Ferreira da Silva, Luís Leitão, Daniel Garcia, Jorge Miguel Sampaio and José Manuel Pires Marques

Phys. Sci. Forum 2026, 13(1), 3; https://doi.org/10.3390/psf2026013003 (registering DOI) - 27 Apr 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Atoms)

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8 pages, 655 KB

Open AccessProceeding Paper

QED Coupling of Image States in Spherical Cavities

by Blake Gerardo Pérez, Renata Della Picca and Juan Martín Randazzo

Phys. Sci. Forum 2026, 13(1), 1; https://doi.org/10.3390/psf2026013001 - 17 Apr 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Atoms)

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3 pages, 148 KB

Open AccessEditorial

Preface and Statement of Peer Review: 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2024)

by Geert Verdoolaege

Phys. Sci. Forum 2025, 12(1), 19; https://doi.org/10.3390/psf2025012019 - 10 Dec 2025

Abstract

n/a Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

8 pages, 5882 KB

Open AccessProceeding Paper

Approaching the Quantum Limit in Axion Detection at IBS-CAPP and IBS-DMAG

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

Phys. Sci. Forum 2025, 11(1), 5; https://doi.org/10.3390/psf2025011005 - 26 Nov 2025

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 19th Patras Workshop on Axions, WIMPs and WISPs)

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7 pages, 2393 KB

Open AccessProceeding Paper

Determination of Uncertainty Model of a Particle-Reflection-Distribution

by Roland Preuss and Udo von Toussaint

Phys. Sci. Forum 2025, 12(1), 18; https://doi.org/10.3390/psf2025012018 - 24 Nov 2025

Abstract

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. [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 291 KB

Open AccessProceeding Paper

Maximum Entropy Production for Optimizing Carbon Catalysis: An Active-Matter-Inspired Approach

by Klaus Regenauer-Lieb, Manman Hu, Hui Tong Chua, Victor Calo, Boris Yakobson, Evgeny P. Zemskov and

Phys. Sci. Forum 2025, 12(1), 16; https://doi.org/10.3390/psf2025012016 - 15 Nov 2025

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 5564 KB

Open AccessProceeding Paper

Bayesian Regularization for Dynamical System Identification: Additive Noise Models

by Robert K. Niven, Laurent Cordier, Ali Mohammad-Djafari, Markus Abel and Markus Quade

Phys. Sci. Forum 2025, 12(1), 17; https://doi.org/10.3390/psf2025012017 - 14 Nov 2025

Abstract

Consider the dynamical system

\dot{x} = f (x)

, where

x \in R^{n}

is the state vector,

\dot{x}

is the time or spatial derivative, and f is the system model. We wish to identify unknown f from its [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 817 KB

Open AccessProceeding Paper

Automatic Modeling and Object Identification in Radio Astronomy

by Richard Fuchs, Jakob Knollmüller and Lukas Heinrich

Phys. Sci. Forum 2025, 12(1), 15; https://doi.org/10.3390/psf2025012015 - 5 Nov 2025

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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8 pages, 1080 KB

Open AccessProceeding Paper

Inverse Bayesian Methods for Groundwater Vulnerability Assessment

by Nasrin Taghavi, Robert K. Niven, Matthias Kramer and David J. Paull

Phys. Sci. Forum 2025, 12(1), 14; https://doi.org/10.3390/psf2025012014 - 5 Nov 2025

Cited by 1

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 1742 KB

Open AccessProceeding Paper

Bayesian Integrated Data Analysis and Experimental Design for External Magnetic Plasma Diagnostics in DEMO

by Jeffrey De Rycke, Alfredo Pironti, Marco Ariola, Antonio Quercia and Geert Verdoolaege

Phys. Sci. Forum 2025, 12(1), 13; https://doi.org/10.3390/psf2025012013 - 4 Nov 2025

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 2230 KB

Open AccessProceeding Paper

Bayesian Functional Data Analysis in Astronomy

by Thomas Loredo, Tamás Budavári, David Kent and David Ruppert

Phys. Sci. Forum 2025, 12(1), 12; https://doi.org/10.3390/psf2025012012 - 4 Nov 2025

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 6055 KB

Open AccessProceeding Paper

WISPFI Experiment: Prototype Development

by Josep Maria Batllori, Michael H. Frosz, Dieter Horns and Marios Maroudas

Phys. Sci. Forum 2025, 11(1), 4; https://doi.org/10.3390/psf2025011004 - 31 Oct 2025

Abstract

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} \sim 50

meV remains largely unexplored by haloscope experiments. We present the first prototype of Weakly Interacting Sub-eV [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 19th Patras Workshop on Axions, WIMPs and WISPs)

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7 pages, 1595 KB

Open AccessProceeding Paper

Progress in GrAHal-CAPP/DMAG for Axion Dark Matter Search in the 1–3 μeV Range

by 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 and SungWoo Youn

Phys. Sci. Forum 2025, 11(1), 3; https://doi.org/10.3390/psf2025011003 - 24 Oct 2025

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 19th Patras Workshop on Axions, WIMPs and WISPs)

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10 pages, 632 KB

Open AccessProceeding Paper

Nonparametric Full Bayesian Significance Testing for Bayesian Histograms

by Fernando Corrêa, Julio Michael Stern and Rafael Bassi Stern

Phys. Sci. Forum 2025, 12(1), 11; https://doi.org/10.3390/psf2025012011 - 20 Oct 2025

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Proceedings of The 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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Journal Description

Physical Sciences Forum

Vol. 14

IOCU 2026

Vol. 13

IOCAT 2026

Vol. 12

MaxEnt 2024

Vol. 11

Axion-Wimp 2024

Vol. 10

IOCP 2024

Vol. 9

MaxEnt 2023

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