arXiv subjects

Gaetano Barone

Publications and source records attributed to Gaetano Barone.

5 recordsLinked to original sources

arXiv2026

Impact of Environmental Stress on Low Gain Avalanche Diode Sensors Response

Low Gain Avalanche Diodes or LGADs are silicon sensors capable of achieving excellent timing resolution due to their characteristic internal gain. Detectors based on LGAD technology play a crucial role in High Energy and Nuclear Physics experiments, among other applications. However, their performance is affected by environmental factors such as temperature, humidity, and storage conditions. A systematic evaluation of the response of LGAD sensors as a function of these environmental parameters is therefore of essential importance for any application. LGAD sensors fabricated at the Brookhaven National Laboratory are characterized and stress-tested against various operating conditions, such as rapid temperature and humidity changes. Dedicated and detailed simulations are used to interpret the experimental results.

physics.ins-det↗

arXiv2026

Machine Learning-Based Reconstruction for Resistive Silicon Sensors

Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer remains unsegmented. This structure provides a 100\% fill factor and enables good spatial resolution with a relaxed readout pitch. The same signal-sharing mechanism that makes interpolation possible complicates the readout: charge spreads across multiple pads, the useful information can approach the electronic-noise threshold, and matrix-inversion approaches can become computationally challenging and sensitive to off-diagonal noise. In this work, we study machine-learning-based reconstruction and compression for resistive silicon sensors. We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information beyond what is available from binary readouts or reduced-amplitude summaries. We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction and have been tested for FPGA deployment using \hls. We also study routes towards bandwidth reduction with waveform rasterisation and window-selection methods, and extend the approach beyond the first model to topology-agnostic transformer-based architectures that use pad coordinates as part of the input. These models are designed to support arbitrary pad counts and geometries, mitigate edge distortions, preserve approximately $10~\mu\mathrm{m}$ position resolution for $500~\mu\mathrm{m}\times500~\mu\mathrm{m}$ pitched sensors, and guide future resistive-silicon sensor designs

hep-ex↗

arXiv2025

Higgs production via vector-boson fusion at the LHC

In this article, we summarise the recent experimental measurements and theoretical work on Higgs boson production via vector-boson fusion at the LHC. Along with this, we provide state-of-the-art predictions at fixed order as well as with parton-shower corrections within the Standard Model at 13.6 TeV. The results are presented in the form of multi-differential distributions as well as in the Simplified Template Cross Section bins. All materials and outputs of this study are available on public repositories. Finally, following findings in the literature, recommendations are made to estimate theoretical uncertainties related to parton-shower corrections.

hep-ph↗

arXiv2024

Ad interim recommendations for the Higgs boson production cross sections at $\sqrt{s} = 13.6$ TeV

This note documents predictions for the inclusive production cross sections of the Standard Model Higgs boson at the Large Hadron Collider at a centre of mass energy of 13.6 TeV. The predictions here are based on simple extrapolations of previously documented predictions published in the CERN Yellow Report "Deciphering the Nature of the Higgs Sector". The predictions documented in this note should serve as a reference while a more complete and update-to-date derivation of cross section predictions is in progress.

hep-ph↗

arXiv2023

PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts

We propose a new approach of jet-based event reconstruction that aims to optimally exploit correlations between the products of a hadronic multi-pronged decay across all Lorentz boost regimes. The new approach utilizes clustered small-radius jets as seeds to define unconventional jets, referred to as PAIReD jets. The constituents of these jets are subsequently used as inputs to machine learning-based algorithms to identify the flavor content of the jet. We demonstrate that this approach achieves higher efficiencies in the reconstruction of signal events containing heavy-flavor jets compared to other event reconstruction strategies at all Lorentz boost regimes. Classifiers trained on PAIReD jets also have significantly better background rejections compared to those based on traditional event reconstruction approaches using small-radius jets at low Lorentz boost regimes. The combined effect of a higher signal reconstruction efficiency and better classification performance results in a two to four times stronger rejection of light-flavor jets compared to conventional strategies at low Lorentz-boosts, and rejection rates similar to classifiers based on large-radius multi-pronged jets at high Lorentz-boost regimes.

hep-ex↗