End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data

TL;DR

This paper introduces an innovative end-to-end deep learning method that utilizes low-level detector data at the CMS experiment to improve particle and event identification at the LHC.

Contribution

It presents a novel deep neural network approach combining detector information for particle discrimination, demonstrating its effectiveness on simulated CMS data.

Findings

Effective discrimination between electrons and photons.

Successful quark vs. gluon classification.

Insights into sub-detector information importance.

Abstract

From particle identification to the discovery of the Higgs boson, deep learning algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider (LHC). We present an innovative end-to-end deep learning approach for jet identification at the Compact Muon Solenoid (CMS) experiment at the LHC. The method combines deep neural networks with low-level detector information, such as calorimeter energy deposits and tracking information, to build a discriminator to identify different particle species. Using two physics examples as references: electron vs. photon discrimination and quark vs. gluon discrimination, we demonstrate the performance of the end-to-end approach on simulated events with full detector geometry as available in the CMS Open Data. We also offer insights into the importance of the information extracted from various sub-detectors and describe how end-to-end techniques can be extended to event-level classification using information from the whole CMS detector.