Improving ATLAS Hadronic Object Performance with ML/AI Algorithms

TL;DR

This paper discusses how ATLAS employs machine learning and AI algorithms to enhance the reconstruction and identification of hadronic objects, improving performance in various tasks at the LHC.

Contribution

It presents selected highlights of ML/AI applications in ATLAS for particle identification, boosted-object detection, and MET reconstruction, showcasing recent advancements.

Findings

Improved particle and boosted-object identification accuracy.

Enhanced MET reconstruction performance.

Demonstrated effectiveness of ML/AI in hadronic object analysis.

Abstract

Hadronic object reconstruction is one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. In this contribution, selected highlights of ML/AI applications by ATLAS to particle and boosted-object identification, MET reconstruction and other tasks are presented.