2024/10/09 by S. Ragoni, Ragoni, Simone, Janet Elizabeth Seger +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · #Cell Image Analysis Techniques #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2410.06983
openalex publication_date 2024/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The increasing data rates in modern high-energy physics experiments such as ALICE at the LHC and the upcoming ePIC experiment at the Electron-Ion Collider (EIC) present significant challenges in real-time event selection and data storage. This paper explores the novel application of machine learning techniques, to enhance the identification of rare low-multiplicity events, such as ultraperipheral collisions (UPCs) and central exclusive diffractive processes. We focus on utilising machine learning models to perform early event classification, even before full event reconstruction, in continuous readout systems. We estimate data rates and disk space requirements for photoproduction and central exclusive diffractive processes in both ALICE and ePIC. We show that machine learning techniques can not only optimize data selection but also significantly reduce storage requirements in continuous readout environments, providing a scalable solution for the upcoming era of high-luminosity particle physics experiments.