Bibliographic Details
| Title: |
GNN-BASED TRACK RECONSTRUCTION FOR MUonE EXPERIMENT. |
| Authors: |
MIZERA, DAMIAN1, GONCERZ, MATEUSZ1,2, JUSZCZAK, IZABELA1, KUCHARCZYK, MARCIN1 marcin.kucharczyk@ifj.edu.pl, WOLTER, MARCIN1, ZDYBAL, MILOSZ1 |
| Source: |
Acta Physica Polonica B. 2026, Vol. 57 Issue 10, p1-15. 15p. |
| Subject Terms: |
*Graph neural networks, *Particle physics, *Railroad tracks, *Pattern perception, *Three-dimensional modeling, *Particle detectors, *Synthetic data |
| Abstract: |
A study of a Graph Neural Network-based model for track reconstruction in the context of MUonE experiment is presented, using simulated data corresponding to the test-run MUonE detector setup. The fully threedimensional model successfully addresses both the reconstruction and particle identification challenges essential for achieving the experiment’s primary physics goal. It provides significantly faster pattern recognition than classical reconstruction algorithms, while maintaining comparable efficiency and resolution. [ABSTRACT FROM AUTHOR] |
| Database: |
Academic Search Index |