Topology classification with deep learning to improve real-time event selection at the LHC

TL;DR

This paper demonstrates how deep learning-based event topology classification can enhance real-time event selection at the LHC, increasing efficiency and reducing false positives to optimize resource use and physics potential.

Contribution

It introduces a deep learning framework for topology classification using raw and high-level data, improving real-time filtering in LHC experiments.

Findings

Achieves ~99% retention of interesting events

Reduces false-positive rate by up to tenfold

Enables more flexible and resource-efficient event selection

Abstract

We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier's score can be trained to retain ~99% of the interesting events and reduce the false-positive rate by as much as one order of magnitude for certain background processes. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could be translated into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments.