Particles

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the end-to-end. AI Detector Optimization framework (AIDO), which leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for a layer arrangement and material composition that align with known calorimeter principles. The success of this proof-of-concept study provides a foundation for the future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments. Full article

The global navigation satellite system (GNSS), using electromagnetic signals, enables continuous positioning throughout the entire surface of the Earth. However, underwater and underground environments significantly restrict the propagation of electromagnetic waves. The sole approach to aid positioning is the utilization of sound signals. Signal blockage in underground and indoor environments demands the accurate location of anchor points for local positioning, which requires previous deployment. Unlike radio waves, the cosmic ray muons are highly reliable natural signal sources for positioning, remaining immune to spoofing and interference. Starting from the standpoint of navigation and positioning, this paper briefly introduces the physical properties of cosmic ray muons and outlines the measurements and positioning principles of muon navigation, including trilateral localization based on the time of flight (TOF) and angular localization based on the angle of arrival (AOA). It subsequently presents the pertinent studies conducted and analyzes the findings. Finally, the challenges of muon navigation are explored from three perspectives: positioning signals, positioning models, and application scenarios. This will offer some new ideas for the domain of localization for further research on muon positioning. Full article

The Pierre Auger Observatory, the world’s largest ultra-high-energy (UHE) cosmic ray (CR) detector, plays a crucial role in multi-messenger astroparticle physics with its high sensitivity to UHE photons and neutrinos. Recent Auger Observatory studies have set stringent limits on the diffuse and point-like fluxes of these particles, enhancing constraints on dark-matter models and UHECR sources. Although no temporal coincidences of neutrinos or photons with LIGO/Virgo gravitational wave events have been observed, competitive limits on the energy radiated in these particles have been established, particularly from the GW170817 binary neutron star merger. Additionally, correlations between the arrival directions of UHECRs and high-energy neutrinos have been explored using data from the IceCube Neutrino Observatory, ANTARES, and the Auger Observatory, providing additional neutrino flux constraints. Efforts to correlate UHE neutron fluxes with gamma-ray sources within our galaxy continue, although no significant excesses have been found. These collaborative and multi-faceted efforts underscore the pivotal role of the Auger Observatory in advancing multi-messenger astrophysics and probing the most extreme environments of the Universe. Full article

Our objective is to test the published models of partonic energy loss, particularly those describing the energy loss mechanisms of quarks traversing nuclear matter, within the framework of semi-inclusive deep inelastic scattering. Our methodological approach focuses on quantifying the quark energy loss in cold matter by analyzing the positive pions (${\pi }^{+}$) produced in various nuclear targets, including deuterium, carbon, iron and lead, while our first approach only includes deuterium and carbon. Before normalizing the pions’ energy distribution to unity to perform a shape analysis, acceptance corrections were performed to account for the detector’s efficiency and ensure accurate comparisons of the spectra. By normalizing the energy spectra of ${\pi }^{+}$ produced from these distinct targets and based on the Baier–Dokshitzer–Mueller–Peigné–Schiff theory, which posits that quark energy loss depends only on nuclear size, it is assumed that the energy distributions of the targets will exhibit similar behavior. For this normalization, an energy shift between these distributions, corresponding to the quark energy loss, is identified. To ensure accuracy, statistical techniques such as the Kolmogorov–Smirnov test are used. The data used to test and explore the analysis technique and method were from the CLAS6 EG2 dataset collected using Jefferson Lab’s CLAS detector. Full article

Muon tomography leverages the small, continuous flux of cosmic rays produced in the upper atmosphere to measure the density of unknown volumes. The multiple Coulomb scattering that muons undergo when passing through the material can either be leveraged or represent a measurement nuisance. In either case, the scattering dependence on muon momentum is a significant source of imprecision. This can be alleviated by including dedicated momentum measurement devices in the experiment, which have a potential cost and can interfere with measurement. An alternative consists of leveraging information on scattering withstood through a known medium. We present a comprehensive study of diverse machine-learning algorithms for this regression task, from classical feature engineering with a fully connected network to more advanced architectures such as recurrent and graph neural networks and transformers. Several real-life requirements are considered, such as the inclusion of hit reconstruction efficiency and resolution and the need for a momentum resolution prediction that can improve reconstruction methods. Full article

We investigated the reaction Q-value ($Q_{\alpha }$) for the $\alpha$ decay of Tl, Bi, and At isotopes using the deformed relativistic Hartree–Bogoliubov theory in continuum (DRHBc) with the covariant density functional PC-PK1. The $\alpha$ decay half-lives of Tl, Bi, and At isotopes are evaluated using various empirical formulas, based on both experimental $Q_{\alpha }$ and those obtained from DRHBc calculations. The calculated $Q_{\alpha }$ and $\alpha$ decay half-lives are compared with experimental data. On the basis of these results, we also predicted the $\alpha$ decay half-lives of isotopes for which experimental data are unavailable. Full article

The Cryo-PoF project is an R&D project funded by the Italian Insitute for Nuclear Research (INFN) in Milano-Bicocca (Italy). The technology at the basis of the project is Power over Fiber (PoF). By sending laser light through an optical fiber, this technology delivers electrical power to a photovoltaic power converter, in order to power sensors or electrical devices. Among the several advantages this solution can provide, we can underline the spark-free operation when electric fields are present, the removal of noise induced by power lines, the absence of interference with electromagnetic fields, and robustness in hostile environments. R&D for the application of PoF in cryogenic environments started at Fermilab in 2020; for the DUNE Vertical Drift detector, it was needed to operate the Photon Detector System on a high-voltage cathode surface. Cryo-PoF, starting from this project, developed a single-laser input line system to power, at cryogenic temperatures, both an electronic amplifier and Photon Detection devices, tuning their bias by means of the input laser power, without adding ancillary fibers. The results obtained in Milano-Bicocca will be discussed, presenting the tests performed using power photosensors at liquid nitrogen temperature. Full article

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase-2 detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits, opening the way to applications of neuromorphic computing to particle tracking. The presented results motivate further studies investigating neuromorphic computing as a potential solution for real-time, low-power particle tracking in future high-energy physics experiments. Full article

Information field theory (IFT) provides a powerful framework for reconstructing continuous fields from noisy and sparse data. Based on Bayesian statistics, IFT allows for the approximation of posterior distributions over field-like parameter spaces in high-dimensional problems. In this contribution, we discuss two applications of IFT in the context of astroparticle physics. First, we present its intended use for the calibration of the newly installed radio detector upgrade of the Pierre Auger Observatory. Second, we demonstrate its application to infer the initial directions of ultra-high-energy cosmic rays before their deflection in the Galactic magnetic field using a simplified model. Full article

This study employs Monte Carlo (MC) models and thermal-statistical analysis to investigate the production mechanisms of strange ($K_S^0$, $\mathrm{\Lambda }$) and multi-strange ($\mathrm{\Xi }$, $\mathrm{\Omega }$) hadrons in high-multiplicity proton–proton collisions. Through systematic comparisons with experimental data, we evaluate the predictive power of EPOS, PYTHIA8, QGSJETII04, and Sibyll2.3d. EPOS, with its hydrodynamic evolution, successfully reproduces low-$p_T$$K_S^0$ and $\mathrm{\Lambda }$ yields in high-multiplicity classes (MC1–MC3), mirroring quark-gluon plasma (QGP) thermalization effects. PYTHIA8’s rope hadronization partially mitigates mid-$p_T$ multi-strange baryon suppression but underestimates $\mathrm{\Xi }$ and $\mathrm{\Omega }$ yields due to the absence of explicit medium dynamics. QGSJETII04, tailored for cosmic-ray showers, overpredicts soft $K_S^0$ yields from excessive soft Pomeron contributions and lacks multi-strange hadron predictions due to enforced decays. Sibyll2.3d’s forward-phase bias limits its accuracy at midrapidity. No model fully captures $\mathrm{\Xi }$ and $\mathrm{\Omega }$ production, though EPOS remains the closest. Complementary Tsallis distribution analysis reveals a distinct mass-dependent hierarchy in the extracted effective temperature ($T_{\mathrm{eff}}$) and non-extensivity parameter (q). As multiplicity decreases, $T_{\mathrm{eff}}$ rises while q declines—a trend amplified for heavier particles. This suggests faster equilibration of heavier particles compared to lighter species. The interplay of these findings underscores the necessity of incorporating QGP-like medium effects and refined strangeness enhancement mechanisms in MC models to describe small-system collectivity. Full article

Bubble nuclei, characterized by a depletion in nucleon density at the nuclear center, are investigated within the atomic number range $71\le Z\le 80$ using the Deformed Relativistic Hartree–Bogoliubov theory in continuum. This study extends previous investigations, which were limited to even–even isotopes, by incorporating even–odd, odd–even, and odd–odd nuclei within this range. The extension is achieved by introducing the blocking effect into the point-coupling approach to ensure self-consistency. Following previous studies, we define a nucleus as a bubble candidate if the bubble parameter exceeds ${\mathcal{B}}_p^{★}=20\%$, and identify five bubble nuclei in both even-Z and odd-Z nuclei groups, based on the highest ${\mathcal{B}}_p^{★}$ values. The formation of bubble structures is confirmed through an analysis of proton single-particle energy levels of the most centrally depleted nuclei across four categories: even–odd, even–even, odd–even, and odd–odd. Full article

The increased particle flux expected at the HL-LHC poses a serious challenge for the ATLAS detector performance, especially in the forward region. The High-Granularity Timing Detector (HGTD), featuring novel Low-Gain Avalanche Detector silicon technology, will provide pile-up mitigation and luminosity measurement capabilities, and augment the new all-silicon Inner Tracker in the pseudo-rapidity range from 2.4 to 4.0. Two double-sided layers will provide a timing resolution better than 50 ps/track for MIPs throughout the HL-LHC running period, and provide a new timing-based handle to assign particles to the correct vertex. The LGAD technology provides suitable gain to reach the required signal-to-noise ratio, and a granularity of 1.3 × 1.3 mm² (with 3.6 M channels in total). This paper presents the current status of the HGTD project with emphasis on the sensor development and module results. Full article

Analysis of the (i) charge balance function and (ii) fluctuations of the net electric charge of hadrons in $Pb$+$Pb$ collisions at center-of-mass energy 2.76 TeV per nucleon pair was performed within a two-component hydjet++ model. It is shown that neither the widths of the balance function nor the strongly intensive quantities, D and Σ, used to describe the net-charge fluctuations, can be reproduced within the model based on a grand canonical ensemble approach for generating multiparticle production. To solve this problem, it is necessary to take into account exact charge conservation in an event-by-event basis. The corresponding procedure was developed and implemented in the modified hydjet++ model. It provides a fair description of the experimental data. Full article

One of the main interests of high-energy physics is the study of the phase diagram and the localization of phase transitions from hadronic to quark–gluonic matter. There are different techniques to study the hot matter. One of them is femtoscopy, which uses two-particle correlations to extract spatiotemporal characteristics of the emission source. Another approach involves obtaining thermodynamic parameters from the momentum distributions of produced particles based on various theoretical models. In this research, we perform a comparative analysis of femtoscopic volumes and volumes obtained using the Tsallis statistical fit. This analysis allows us to estimate system size at the time of kinetic freeze-out and its dependence on collision centrality and energy. We observe that at high energies, the volume values estimated taking the two approaches diverge significantly, while at low energies, they are more consistent. In the future, these results can help to combine these two different methods and provide a more comprehensive picture of the fireball produced in heavy-ion collisions. Full article

The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a constrained muon flux, leading to prolonged acquisition times, noisy reconstructions, and challenges in image interpretation. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images were able to match the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated a noise improvement to that of 31 days worth of sampling. A second cWGAN-GP model, trained for semantic segmentation, was developed to quantitatively assess the upsampling model’s impact on each of the features within the concrete samples. This model was able to achieve segmentation of rebar grids and tendon ducts embedded in the concrete, with respective Dice–Sørensen accuracy coefficients of 0.8174 and 0.8663. This model also revealed an unexpected capability to mitigate—and in some cases entirely remove—z-plane smearing artifacts caused by the muography’s inherent inverse imaging problem. Both models were trained on a comprehensive dataset generated through Geant4 Monte Carlo simulations designed to reflect realistic civil infrastructure scenarios. Our results demonstrate significant improvements in both acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications. Full article

The shape of a nucleus is one of its fundamental properties. We conduct a systematic investigation of shape coexistence in odd-Z nuclei from fluorine to potassium using the deformed relativistic Hartree–Bogoliubov theory in continuum. First, we present a simple argument regarding the energy differences between degenerate vacua, which can serve as a criterion for identifying candidates for shape coexistence. We then predict isotopes that exhibit shape coexistence. Full article

Investigating the production of Higgs boson pairs (HH) at the LHC provides critical insights into the self-interaction properties of the Higgs boson, representing an essential verification of the Standard Model and contributing to our understanding of the Higgs boson properties. This work highlights the latest findings from the CMS collaboration on HH production measurements. These searches include different final states and integrate data collected by the CMS experiment at a center-of-mass energy of 13 TeV. Full article

We investigate the phenomenological effects of the nonholomorphic (NH) higgsino mass term, ${\mu }^{\prime }$, within the minimal supersymmetric standard model (MSSM) extended by a non-abelian flavor symmetry, referred to as the sNHSSM. This flavor symmetry enables a substantial reduction in the number of free parameters inherent to the MSSM, streamlining them from a large set to just eight. Our study explores the interplay between cold dark matter (CDM) relic density (${\mathsf{\Omega }}_{\mathrm{CDM}}{h}^2$) and the anomalous magnetic moment of the muon, ${(g-2)}_{\mu }$. We study correlations among the theoretical parameters that emerge from this interplay and are further constrained by experimental data such as the Higgs boson mass, B-physics observables, and the charge and color breaking minima constraints. Moreover, our findings reveal that incorporating the NH higgsino mass term opens up new regions of parameter space that were previously inaccessible. Full article

In this work, we present an overview of the recent results, obtained in the framework of the fractional analytic QCD in the space-like (Euclidean) and time-like regions. The Higgs boson decays into a bottom–antibottom pair, and the polarized Bjorken sum rule is considered as an application of the obtained results. Full article