arXiv subjects

A. Ciardiello

Publications and source records attributed to A. Ciardiello.

3 recordsLinked to original sources

arXiv2026

Radiation tolerance tests on key components of the ePIC-dRICH readout card

The dual-radiator RICH (dRICH) detector of the ePIC experiment will employ over 300000 SiPM pixels as photosensors, organized into more than 1000 Photon Detection Units. Each PDU is a compact module, approximately 5x5x12 cm^3 in size, including four custom ASICs connected to 256 SiPMs and an FPGA-based readout card (RDO) responsible for data acquisition and control. Considering the moderately harsh radiation environment expected in the dRICH detector, this study reports on proton irradiation tests performed on key components of the RDO card to assess their tolerance to cumulative Total Ionizing Dose (TID) and Single Event Effects (SEE). All tested components demonstrated radiation tolerance beyond the TID levels expected for the dRICH environment, with the exception of the ATtiny817 microcontroller, which showed destructive failure. Furthermore, as expected, the observed Single Event Upset (SEU) rates call for appropriate mitigation strategies in the final system design.

physics.ins-det↗

arXiv2022

Progress report on the online processing upgrade at the NA62 experiment

A new FPGA-based low-level trigger processor has been installed at the NA62 experiment. It is intended to extend the features of its predecessor due to a faster interconnection technology and additional logic resources available on the new platform. With the aim of improving trigger selectivity and exploring new architectures for complex trigger computation, a GPU system has been developed and a neural network on FPGA is in progress. They both process data streams from the Ring Imaging Cherenkov detector of the experiment to extract in real time high level features for the trigger logic. Description of the systems, latest developments and design flows are reported in this paper.

physics.ins-det↗

arXiv2020

Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model

Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB ("Boltzmann-Langevin One Body"), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also $^{12}$C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of $^{12}$C with $^{12}$C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter ($b$) training a classifier of $b$ jointly with the VAE. Results: The distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.

physics.comp-ph↗