arXiv subjects

Marie Vanstalle

Publications and source records attributed to Marie Vanstalle.

3 recordsLinked to original sources

arXiv2026

Measurement of 80-200 MeV/n $^{16}$O nuclear cross-section on Carbon and Polyethylene targets with the nuclear emulsion detector of the FOOT experiment

Accurate knowledge of nuclear fragmentation cross-sections is essential for optimizing charged particle therapy. In this study, conducted within the framework of the FOOT (FragmentatiOn Of Target) experiment, we present the first measurements with a large angular acceptance of total charge-changing cross-section and the cross-section for the production of fragments (production cross-section) for $^{16}$O ions interacting with Carbon (C) and Polyethylene (C$_2$H$_4$) targets in the kinetic energy range of 80 to 200 MeV/nucleon. Measurements were performed using the Emulsion Cloud Chamber (ECC) technique, which combines high spatial resolution and angular acceptance, up to 45$^\circ$. The results are compared with Monte Carlo model predictions. Moreover, the total charge-changing and fragment production cross-sections for $^{16}$O on Hydrogen in the same energy range are derived.

nucl-ex↗

arXiv2025

Enhancing nuclear cross-section predictions with deep learning: the DINo algorithm

Accurate modeling of nuclear reaction cross-sections is crucial for applications such as hadron therapy, radiation protection, and nuclear reactor design. Despite continuous advancements in nuclear physics, significant discrepancies persist between experimental data and theoretical models such as TENDL, and ENDF/B. These deviations introduce uncertainties in Monte Carlo simulations widely used in nuclear physics and medical applications. In this work, DINo (Deep learning Intelligence for Nuclear reactiOns) is introduced as a deep learning-based algorithm designed to improve cross-section predictions by learning correlations between charge-changing and total cross-sections. Trained on the TENDL-2021 dataset and validated against experimental data from the EXFOR database, DINo demonstrates a significant improvement in predictive accuracy over conventional nuclear models. The results show that DINo systematically achieves lower chi2 values compared to TENDL-2021 across multiple isotopes, particularly for proton-induced reactions on a 12C target. Specifically, for 11C production, DINo reduces the discrepancy with experimental data by \sim 28\% compared to TENDL-2021. Additionally, DINo provides improved predictions for other relevant isotopes produced, such as 4He, 6Li, 9Be, and 10B, which play a crucial role in modeling nuclear fragmentation processes. By leveraging neural networks, DINo offers fast cross-section predictions, making it a promising complementary tool for nuclear reaction modeling. However, the algorithm's performance evaluation is sensitive to the availability of experimental data, with increased uncertainty in sparsely measured energy ranges. Future work will focus on refining the model through data augmentation, expanding its applicability to other reaction channels, and integrating it into Monte Carlo transport codes for real-time nuclear data processing.

physics.comp-ph↗

arXiv2020

The FragmentatiOn Of Target Experiment (FOOT) and its DAQ system

The FragmentatiOn Of Target (FOOT) experiment aims to provide precise nuclear cross-section measurements for two different fields: hadrontherapy and radio-protection in space. The main reason is the important role the nuclear fragmentation process plays in both fields, where the health risks caused by radiation are very similar and mainly attributable to the fragmentation process. The FOOT experiment has been developed in such a way that the experimental setup is easily movable and fits the space limitations of the experimental and treatment rooms available in hadrontherapy treatment centers, where most of the data takings are carried out. The Trigger and Data Acquisition system needs to follow the same criteria and it should work in different laboratories and in different conditions. It has been designed to acquire the largest sample size with high accuracy in a controlled and online-monitored environment. The data collected are processed in real-time for quality assessment and are available to the DAQ crew and detector experts during data taking.

physics.ins-det↗