Ruhr Hadron Seminar

Track Reconstruction Using Cellular Automata for the High Luminosity LHC & AI-Based QA Monitoring for the CBM experiment

Name: Track Reconstruction Using Cellular Automata for the High Luminosity LHC & AI-Based QA Monitoring for the CBM experiment
Start: 2026-05-05T15:00:00+02:00
End: 2026-05-05T15:45:00+02:00
Location: RUB

by Sachin Gupta (GSI)

Tuesday May 5, 2026, 3:00 PM → 3:45 PM Europe/Zurich

NB2/158 (RUB)

NB2/158

RUB

Universitätsstraße 150, 44801 Bochum

Description

Abstract:

CERN is currently preparing for the HL-LHC phase. Track reconstruction is a computationally expensive task, which becomes increasingly challenging in high pile-up environments. This thesis explores a cellular automaton–based tracking algorithm with triplet fitting, which is parallelizable and suitable for GPU implementation, unlike the iterative CKF. The method achieves ~96% efficiency and ~99% purity for tracks in the barrel region.

The CBM experiment is a complex detector system composed of numerous subdetectors operating simultaneously and generating thousands of monitoring plots to ensure stable and reliable performance. This project proposes the adoption of an AI-based detection framework, HYDRA, originally developed for the GlueX experiment at Jefferson Lab. HYDRA employs computer-vision models for near–real-time image classification to recognize irregular patterns in detector monitoring outputs. The project focuses on adapting and deploying HYDRA for the CBM experiment.