Machine learning opportunities for online and offline tagging of photo-induced and diffractive events in continuous readout experiments

TL;DR

This paper investigates how machine learning can improve real-time event classification and data management in high-energy physics experiments with continuous readout, focusing on rare event detection and storage optimization.

Contribution

It introduces novel machine learning applications for early event classification in continuous readout systems, reducing data storage needs in future high-luminosity experiments.

Findings

Machine learning enhances early event identification in continuous readout.

Significant reduction in data storage requirements achieved.

Potential for scalable real-time data processing in high-energy physics.

Abstract

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.