Machine learning for hadron spectroscopy (Cesar Fernandez Ramirez, UNAM)

Novembre 10, 2022@11:00 am–12:00 pm
Aula 602 e online

Seminario di Fenomenologia

Recently, JPAC collaboration has developed and benchmarked a systematic approach to use Deep Neural Networks as a model-independent tool to analyze and interpret experimental data and to determine the nature of an exotic hadron. Specifically, we studied the line shape of the Pc(4312) signal reported by the LHCb collaboration. This novel method presents great potential and can be applied to other near-threshold resonance candidates.

Per connettersi a zoom
Topic: Theory and Pheno seminars
Meeting ID: 857 318 5271