arXiv subjects

Giacomo Borghi

Publications and source records attributed to Giacomo Borghi.

At least 19 recordsLinked to original sources

arXiv2026

Pulse-shape discrimination with machine learning for CZT detectors at the DA$Φ$NE beam test facility

Cadmium zinc telluride (CZT) detectors offer versatility, operational simplicity, and room-temperature X- and gamma-ray spectroscopy, making them attractive for collider applications, yet their use under high-flux conditions remains limited. Here, we present a preliminary feature-based pulse-shape analysis employing machine learning, on data acquired with a quasi-hemispherical CZT detector at the DA$\Φ$NE beam test facility of the National Laboratory of Frascati of INFN. A 300-MeV electron beam impinging on a lead target produced characteristic Pb X-rays together with a broad background extending up to the electron-positron annihilation region. Physically motivated temporal and morphological features were extracted from the recorded waveforms and used to distinguish nominal photon-like pulses from anomalous events. An XGBoost classifier trained and validated on 10,000 labeled waveforms achieved an accuracy of approximately 97%, with most of its classification performance reached using only a few hundred labeled examples. The trained model was applied to more than 700,000 events, substantially reducing the spectral continuum and coincidence peaks, while preserving the characteristic Pb X-ray lines up to the 511-keV annihilation peak. These preliminary results demonstrate the potential of machine-learning-assisted pulse-shape discrimination for improving CZT spectroscopy in collider environments.

physics.ins-det↗

arXiv2026

A fast spectral particle method for the Landau equation

We propose a fast deterministic particle method for the spatially homogeneous Landau equation. The method combines a particle representation of the solution with a Fourier approximation of the nonlinear collision flux. Nonuniform fast Fourier transforms are used to reconstruct the density from the particles and to evaluate the flux and density at the particle locations, while the convolutional structure of the flux enables its efficient computation by FFTs. For a fixed transform tolerance, the cost per time step is $\mathcal{O}(N+M^d\log M)$, where $N$ is the number of particles and $M$ the number of Fourier modes per velocity dimension. We establish a consistency estimate for the reconstructed velocity field on regions where the reference density is bounded away from zero. The estimate separates the spectral truncation error from the particle-density reconstruction error, showing how the latter can dominate for sufficiently smooth densities. Two-dimensional numerical experiments with Maxwellian interactions illustrate the accuracy and efficiency of the method, its sensitivity to particle and spectral resolution, and the effects of filtering. Comparisons with a direct blob implementation demonstrate improved accuracy at a strongly reduced computational cost.

math.NA↗

arXiv2026

First measurement of kaonic deuterium X-ray transitions

The study of the strong interaction among hadrons at low energies remains one of the key challenges in fundamental physics because of its non-perturbative nature, which makes theoretical descriptions strongly dependent on experimental input. Although substantial progress has been made for systems involving up and down quarks, theoretical models in the strangeness sector continue to face limitations due to the lack of experimental data. Kaonic atoms provide a powerful tool to study the low-energy strong interaction with strangeness through the energy shifts and widths induced on their lowest atomic levels. In this context, kaonic deuterium X-ray spectroscopy has long represented one of the major open challenges in hadronic-atom physics because of its extremely low X-ray yield. This measurement is particularly important because it gives access to the experimentally inaccessible $K^-n$ interaction at threshold energy. Here, we report the first observation of kaonic deuterium X-ray transitions, performed with the SIDDHARTA-2 experiment at the DA$Φ$NE collider. We determine the strong-interaction shift and width of the $1s$ level to be $\varepsilon_{1s}=-810.9\pm24.5\,(\mathrm{stat})\pm2.1\,(\mathrm{syst})\,\mathrm{eV}$ and $Γ_{1s}=812\pm97\,(\mathrm{stat})\pm33\,(\mathrm{syst})\,\mathrm{eV}$, respectively. This measurement constitutes the most precise experimental determination of the $K^-d$ strong interaction at threshold and allows discrimination among competing theoretical models. Combined with the kaonic hydrogen measurement, this result provides the experimental input required to determine the isospin-dependent $K^-N$ scattering lengths, with implications for the description of the nature of the first predicted hadronic molecular state, the $Λ(1405)$, and neutron-rich matter.

nucl-ex↗

arXiv2026

Long-time Stability and Convergence of Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a global optimization algorithm defined by an interacting set of particles evolving over the search space. Heuristically motivated, its theoretical analysis remains limited due to the second-order, stochastic, and highly nonlinear nature of the dynamics. In this paper, we connect classical PSO stability analysis under the stagnation assumption with more recent mean-field methods, providing new quantitative estimates for the time-discrete algorithm. We study in particular a regularized PSO model without memory, with non-degenerate noise by adding a noise floor to the original model. Studying such a surrogate model allows us to identify quantitative conditions under which the dynamics is stable and converges toward a small neighborhood of a global minimizer. We do so by first studying the Schur stability of the linearized dynamics, then analyzing the convergence properties of a nonlinear mean-field system via a Laplace principle, and finally establishing a quantitative error bound for the mean-field approximation of order $N^{-1/2}$.

math.OC↗

arXiv2026

Kaonic Copper and Fluorine Absolute Yields Measurement with a CZT-based Detection System at DA$Φ$NE

\noindent In this work, new measurements of absolute X-ray yields for several transitions in kaonic copper and, for the first time, in kaonic fluorine are reported. The data were collected by the SIDDHARTA-2 collaboration at the DA$Φ$NE collider using a novel room-temperature Cadmium Zinc Telluride (CZT) detection system. Detection efficiencies were evaluated through a dedicated Geant4 Monte Carlo simulation of the full experimental setup, enabling the extraction of absolute yields per stopped kaon. \noindent The measured yields exhibit a systematic dependence on the principal quantum number, reflecting the interplay between radiative transitions, Auger de-excitation, and strong-interaction-induced nuclear capture. In kaonic fluorine, a suppression of the 4$\to$3 transition yield relative to higher-n transitions is observed, providing evidence for the onset of strong-interaction effects already at the $n=4$ level. From this behaviour, a conservative lower limit on the corresponding strong-interaction width is derived. \noindent These results provide new quantitative constraints for cascade models of exotic atoms and extend experimental access to intermediate atomic levels where strong-interaction effects are not directly observable via level shifts and widths. They also establish CZT-based detection as a powerful and versatile approach for high-resolution X-ray spectroscopy of kaonic atoms in collider environments.

nucl-ex↗

arXiv2026

Variational inference via Gaussian interacting particles in the Bures-Wasserstein geometry

Motivated by variational inference methods, we propose a zeroth-order algorithm for solving optimization problems in the space of Gaussian probability measures. The algorithm is based on an interacting system of Gaussian particles that stochastically explore the search space and self-organize around global minima via a consensus-based optimization (CBO) mechanism. Its construction relies on the Linearized Bures-Wasserstein (LBW) space, a novel parametrization of Gaussian measures we introduce for efficient computations. LBW is inspired by linearized optimal transport and preserves key geometric features while enabling computational tractability. We establish well-posedness and study the convergence properties of the particle dynamics via a mean-field approximation. Numerical experiments on variational inference tasks demonstrate the algorithm's robustness and superior performance with respect to deterministic gradient-based method in presence of low-dimensional non log-concave targets.

math.OC↗

arXiv2026

Probing Nuclear Structure with Kaonic Atoms through E2 Resonance Mixing

Kaonic atoms provide a unique laboratory to investigate the interplay between atomic, nuclear, and strong-interaction physics. In heavy nuclei, atomic transitions can couple to low-lying collective nuclear excitations via the electric quadrupole interaction. When the energy difference between two kaonic atomic levels approaches that of a nuclear $2^+$ excitation, a resonant configuration mixing may occur, known as the E2 nuclear resonance effect. In this work, we investigate the conditions for E2 resonance in kaonic molybdenum isotopes. We describe the mixing using state-of-the-art Dirac-Fock calculations combined with updated nuclear structure inputs, including recent electric quadrupole transition strength values and excitation energies. We evaluate the sensitivity of the effect to key parameters, assess its observability in future experiments such as the EXKALIBUR program, and discuss its impact on cascade dynamics. Our results demonstrate the potential of kaonic atoms as a probe of nuclear structure, complementary to conventional nuclear spectroscopy.

nucl-ex↗

arXiv2026

Two-Time-Scale Learning Dynamics: A Population View of Neural Network Training

Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast within-model optimisation with slower population-level adaptation. Despite their empirical success, a general mathematical description of the resulting collective training dynamics remains incomplete. We introduce a theoretical framework for neural network training based on two-time-scale population dynamics. We model a population of neural networks as an interacting agent system in which network parameters evolve through fast noisy gradient updates of SGD/Langevin type, while hyperparameters evolve through slower selection--mutation dynamics. We prove the large-population limit for the joint distribution of parameters and hyperparameters and, under strong time-scale separation, derive a selection--mutation equation for the hyperparameter density. For each fixed hyperparameter, the fast parameter dynamics relaxes to a Boltzmann--Gibbs measure, inducing an effective fitness for the slow evolution. The averaged dynamics connects population-based learning with bilevel optimisation and classical replicator--mutator models, yields conditions under which the population mean moves toward the fittest hyperparameter, and clarifies the role of noise and diversity in balancing optimisation and exploration. Numerical experiments illustrate both the large-population regime and the reduced two-time-scale dynamics, and indicate that access to the effective fitness, either in closed form or through population-level estimation, can improve population-level updates.

cs.LG↗

arXiv2026

CZT Detectors for kaonic atoms spectroscopy

Cadmium zinc telluride (CZT) detectors offer excellent room-temperature energy resolution, making them well suited for X- and $γ$-ray spectroscopy in challenging environments. Within the SIDDHARTA-2 program at the DA$Φ$NE collider, a new CZT-based detection system has been developed to enable precision measurements of kaonic atom transitions in the intermediate mass range. In this work, we report the results of a calibration campaign performed with the collider operating, aimed at assessing the detector performance. A dedicated setup, including an array of quasi-hemispherical CZT sensors and a $^{152}$Eu source, was used to characterize the spectral response. The reconstructed emission lines were fitted with a model accounting for Gaussian response and incomplete charge recollection tails, and the detector linearity was evaluated by comparing the measured peak positions with their nominal energies. The results demonstrate that the CZT detector exhibits excellent linearity and stable operation with the collider on, confirming its suitability for future kaonic-atom spectroscopy at DA$Φ$NE.

physics.ins-det↗

arXiv2026

Extended X-ray energy characterization of SIDDHARTA-2 large-area Silicon Drift Detectors up to 50 keV

The SIDDHARTA-2 experiment at the DA$Φ$NE collider of INFN-LNF performs high precision light kaonic atoms X-ray spectroscopy to investigate the kaon-nucleon(s) strong interaction in the low-energy (O(10 keV)) regime. A large area Silicon Drift Detectors (SDDs) system has been developed to carry out these measurements. The collaboration aims to extend the measurements campaign to higher mass kaonic atoms, which exhibit transition lines at increased X-ray energies. In this context, the spectroscopic response of the SIDDHARTA-2 SDD system was investigated in terms of linearity and energy resolution up to 50 keV. An accuracy of the energy calibration procedure $ΔE/E < 10^{-3}$ was achieved.

physics.ins-det↗

arXiv2026

Chaos propagation in genetic algorithms: An optimal transport approach

Genetic algorithms are high-level heuristic optimization methods which enjoy great popularity thanks to their intuitive description, flexibility, and, of course, effectiveness. The optimization procedure is based on the evolution of possible solutions following three mechanisms: selection, mutation, and crossover. In this paper, we look at the algorithm as an interacting particle system and show that it is described by a Boltzmann-type equation in the many particles limit. Specifically, we prove a propagation of chaos result with a novel technique that leverages the optimal transport formulation of the bounded Lipschitz norm and naturally incorporates the crossover mechanism into the analysis. The convergence admits a rate with respect to the number of particles, corresponding to the optimal rate in the Wasserstein-1 distance.

math.PR↗

arXiv2025

Time-based Selection of Kaonic Atom X-ray Events with Quasi-Hemispherical CZT Detectors at the DAFNE collider

This work presents the results of a time-based event selection for searching X-ray signals from kaonic atom X-ray transition using a single quasi-hemispherical Cadmium-Zinc-Telluride (CZT) detector at the DA$Φ$NE collider. To mitigate the high background level in the measured X-ray spectrum, a dedicated event selection strategy was developed, exploiting the precise timing correlation between e+e- collisions and detector signals. This approach enabled, for the first time, the observation of two characteristic X-ray transitions from kaonic aluminum atoms using a CZT detector: for the 5-4 transition at 50~keV, 362~$\pm$~41~(stat.)~$\pm$~20~(sys.) signal events over 1698~$\pm$~197~(stat.)~$\pm$~25~(sys.) background events in 5$σ$ were observed, with a resolution of 9.2\%~FWHM; for the 4-3 transition at 106~keV, 295~$\pm$~50~(stat.)~$\pm$~20~(sys.) signal events over 2939~$\pm$~500~(stat.)~$\pm$~16~(sys.) background events in 5$σ$ were measured, with a resolution of 6.6 ~FWHM. A strong background suppression of approximately 95\% of the triggered data was achieved through this time-based selection. The demonstrated timing capability of the CZT detector proved highly effective in isolating time-correlated events within an 80 ns window, setting an important benchmark for the application of these semiconductors in timing-based X-ray spectroscopy. These results highlight the potential of CZT-based detection systems for future precision measurements in high-radiation environments, paving the way for compact, room-temperature X-ray and $γ$-ray spectrometers in fundamental physics and beyond.

physics.ins-det↗

arXiv2025

EXKALIBUR: Towards a Kaonic Atoms Periodic Table to test Fundamental Interactions

Kaonic atoms, formed when a negatively charged kaon replaces an electron, provide a unique laboratory to test fundamental interactions at low energies. EXKALIBUR (EXtensive Kaonic Atoms research: from LIthium and Beryllium to URanium) is a program to perform systematic, high-precision X-ray spectroscopy of selected kaonic atoms across the periodic table at the DA$Φ$NE accelerator at the National Laboratory of Frascati (INFN-LNF). Here, we outline its detector-driven strategy: Silicon Drift Detectors for 10-40 keV transitions in light targets (Li, Be, B, O), CdZnTe detectors for 40-300 keV lines in intermediate-$Z$ systems (Mg, Al, Si, S), and a High-Purity Germanium detector for high-$Z$ atoms (Se, Zr, Ta, Mo, W, Pb), complemented by VOXES, a high-resolution crystal spectrometer for sub-eV studies. EXKALIBUR plans to (i) reduce the charged-kaon mass uncertainty below 10 keV, (ii) produce a database of nuclear shifts and widths to constrain multi-nucleon K$^{-}$-nucleus interaction models, and (iii) provide precision data for testing bound-state QED in strong fields. We summarize the planned measurements and expected sensitivities within DA$Φ$NE luminosities.

nucl-ex↗

arXiv2025

Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis

Metaheuristic algorithms are powerful tools for global optimization, particularly for non-convex and non-differentiable problems where exact methods are often impractical. Particle-based optimization methods, inspired by swarm intelligence principles, have shown effectiveness due to their ability to balance exploration and exploitation within the search space. In this work, we introduce a novel particle-based optimization algorithm where velocities are updated via random jumps, a strategy commonly used to enhance stochastic exploration. We formalize this approach by describing the dynamics through a kinetic modelling of BGK type, offering a unified framework that accommodates general noise distributions, including heavy-tailed ones like Cauchy. Under suitable parameter scaling, the model reduces to the Consensus-Based Optimization (CBO) dynamics. For non-degenerate Gaussian noise in bounded domains, we prove propagation of chaos and convergence towards minimizers. Numerical results on benchmark problems validate the approach and highlight its connection to CBO.

math.OC↗

arXiv2025

Wasserstein convergence rates for stochastic particle approximation of Boltzmann models

We establish quantitative convergence rates for stochastic particle approximation based on Nanbu-type Monte Carlo schemes applied to a broad class of collisional kinetic models. Using coupling techniques and stability estimates in the Wasserstein-1 (Kantorovich-Rubinstein) metric, we derive sharp error bounds that reflect the nonlinear interaction structure of the models. Our framework includes classical Nanbu Monte Carlo method and more recent developments as Time Relaxed Monte Carlo methods. The results bridge the gap between probabilistic particle approximations and deterministic numerical error analysis, and provide a unified perspective for the convergence theory of Monte Carlo methods for Boltzmann-type equations. As a by-product, we also obtain existence and uniqueness of solutions to a large class of Boltzmann-type equations.

math.NA↗

arXiv2024

Intermediate Mass Kaonic Atoms at DA$Φ$NE

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possibility to measure transition lines coming from intermediate mass kaonic atoms, such as kaonic carbon and kaonic aluminum. In order to measure transitions where strong interaction is manifesting at higher energies, out of the energy range of the SIDDHARTA-2 apparatus, the collaboration is testing a new detector system which exploits a novel compound semiconductor, the Cadmium Zinc Telluride. Tests are now running at DA$Φ$NE to study the performance of this detector, exploring the possibility to build a dedicated setup.

nucl-ex↗

arXiv2024

First Linearity and Stability Characterization for CZT Detection System in a e$^+$e$^-$ Collider Environment

The SIDDHARTA-2 collaboration built a new cadmium-zinc-telluride (CZT, CdZnTe)-based X-ray detection system, used for the first time in the DA$Φ$NE electron-positron collider at INFN-LNF. The aim of this work is to show that these detectors present optimal long- and short-term linearity and stability to perform precise spectroscopic measurements in a collider environment. The spectra used as references for calibration are reported, and the results about the linearity and stability studies are presented. It is also discussed and showed what is the proper function to describe all the effects that alter the Gaussian shape in semiconductors, particularly evident in the CZT case. Good residuals and resolutions were obtained for all the calibrations. In a test run with the source and the collider beam on, it was demonstrated that the calibrations made with beam off are optimal also when the beam is on, and the actual systematics in a physics run were estimated. These promising results show the potentialities of this detector in the high rate environment of a particle collider, and pave the way for the use of CZT detectors in kaonic atoms researches and in accelerators, with applications for particle and nuclear physics.

physics.ins-det↗

arXiv2024

Kinetic models for optimization: a unified mathematical framework for metaheuristics

Metaheuristic algorithms, widely used for solving complex non-convex and non-differentiable optimization problems, often lack a solid mathematical foundation. In this review, we explore how concepts and methods from kinetic theory can offer a potential unifying framework for a variety of metaheuristic optimization methods. By applying principles from collisional and non-collisional kinetic theory, we outline how particle-based algorithms like Simulated Annealing, Genetic Algorithms, Particle Swarm Optimization, and Ensemble Kalman Filter may be described through a common statistical perspective. This approach not only provides a path to deeper theoretical insights and connects different methods under suitable asymptotic scalings, but also enables the derivation of novel algorithms using alternative numerical solvers. While not exhaustive, our review highlights how kinetic models can enhance the mathematical understanding of existing optimization algorithms and inspire new computational strategies.

math.OC↗