arXiv subjects

Ludovico Massaccesi

Publications and source records attributed to Ludovico Massaccesi.

3 recordsLinked to original sources

arXiv2026

Gain-Layer Project

Gain-layer degradation from exposure to radiation limits the use of Low-Gain Avalanche Diodes (LGADs) in high energy particle physics detector experiments. Proper understanding of how the gain-layer is destroyed is not available on a defect level. Only measurements for materials with much lower effective doping concentrations are available. The direct study of the gain-layer is not possible with typical defect spectroscopy measurements like Thermally Stimulated Currents (TSC) and Deep-Level Transient Spectroscopy (DLTS). To combat this problem and gain a better understanding of the processes which degrade LGADs, the Gain-Layer Project was started. This project produced 19050 diodes with various Boron, Phosphorus, Oxygen and Carbon concentrations. The material used is low-resistivity p-type Silicon. The effective doping concentrations are in the order of a LGAD gain-layer. These diodes will serve the defect community in the coming years for various studies. This article introduces this project with detailed descriptions of the diodes, their flavours and their processing, and reports on results from I-V, C-V, SIMS and DLTS measurements on unirradiated diodes.

physics.ins-det↗

arXiv2026

Kink Finder at Belle II

We present a track-finding algorithm for the Belle II experiment that specifically targets so-called kinks: signatures of charged particles decaying or scattering in-flight in the detector material, resulting in a sudden and significant change of the particle's flight direction. Our benchmark studies of this Kink Finder show that the reconstruction efficiency for such signatures is about 40%, compared to a value of around 11% for the standard Belle II track-finding algorithm. Our studies also show that the Kink Finder significantly improves the resolution of the secondary track parameters, suppresses the number of cloned tracks, and reduces the PID misidentification rates for kaon and pions.

hep-ex↗

arXiv2025

End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II

We present the study of an end-to-end multi-track reconstruction algorithm for the central drift chamber of the Belle II experiment at the SuperKEKB collider using Graph Neural Networks for an unknown number of particles. The algorithm uses detector hits as inputs without pre-filtering to simultaneously predict the number of track candidates in an event and their kinematic properties. In a second step, we cluster detector hits for each track candidate to pass to a track fitting algorithm. Using a realistic full detector simulation including beam-induced backgrounds and detector noise taken from actual collision data, we find significant improvements in track finding efficiencies for tracks in a variety of different event topologies compared to the existing baseline algorithm used in Belle II. For events with a hypothetical long-lived massive particle with a mass in the GeV-range decaying uniformly along its flight direction into two charged particles, the GNN achieves a combined track finding and fitting efficiency of 85.4% with a fake rate of 2.5%, compared to 52.2% and 4.1% for the baseline algorithm. This is the first end-to-end multi-track machine learning algorithm for a drift chamber detector that has been utilized in a realistic particle physics environment.

physics.ins-det↗