Machine learning in online and offline reconstruction and identification with CMS

TL;DR

This paper reviews how machine learning techniques are enhancing event reconstruction and particle identification in CMS, improving sensitivity and efficiency across various detector components and preparing for future high-luminosity conditions.

Contribution

It presents new ML algorithms for CMS that improve object identification, flavor tagging, and energy reconstruction, including strategies for future detector upgrades.

Findings

Enhanced jet flavor tagging sensitivity to Higgs decays

Significant improvements in tau and muon identification

Development of ML algorithms for high-luminosity detector upgrades

Abstract

Machine learning (ML) plays an increasingly important role in both online and offline event reconstruction and identification at CMS experiment. A variety of ML techniques are used to improve the identification of physics objects. Dedicated algorithms enhance jet flavor tagging, including new approaches that strengthen sensitivity to Higgs boson decays to charm quarks. Tau identification has been significantly improved with ML-based methods, while in the electromagnetic calorimeter, ML-driven clustering techniques provide better energy reconstruction. Muon identification also benefits from multivariate approaches, leading to a higher signal efficiency and more background rejection. Looking at the future, ML will be central to the reconstruction strategy for the High-Granularity Calorimeter at high-luminosity LHC. New algorithms for the upgraded detectors are being developed to cope with extreme pileup conditions. All these advances ensure that CMS can fully exploit the physics potential of Run-3 and the HL-LHC, while also exploring novel ML strategies to maintain robust performance under evolving experimental conditions.