2 citations · 4 across the 4 of their papers we have counts for
5 papers
Track finding and particle identification performance with particle physics foundation models
Shuhang Li, Haiwang Yu, Gabor Galgoczi +9
Modern high energy nuclear and particle physics experiments produce large amounts of sparse data that must be reduced significantly in size before performing physics analysis. Rece…
CODEX-b: Opening New Windows to the Long-Lived Particle Frontier at the LHC
Giulio Aielli, Juliette Alimena, Saul Balcarcel-Salazar +50
This document is written as a contribution to the European Strategy of Particle Physics (ESPP) update. We offer a detailed overview of current developments and future directions fo…
Technical design report for the CODEX- demonstrator
b collaboration, Giulio Aielli, Juliette Alimena +56
The CODEX- apparatus is a demonstrator for the proposed future CODEX-b experiment, a long-lived-particle detector foreseen for operation at IP8 during HL-LHC data-taking. The de…
Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC
Simon Akar, Mohamed Elashri, Rocky Bala Garg +6
We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary…
Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices
Simon Akar, Michael Peters, Henry Schreiner +2
Using deep neural networks to identify and locate proton-proton collision points, or primary vertices, in LHCb has been studied for several years. Preliminary results demonstrated…