Improving ATLAS Hadronic Object Performance with ML/AI Algorithms

Experimental uncertainties related to hadronic object reconstruction can limit the precision of physics analyses at the LHC, and so improvements in performance have the potential to broadly increase the impact of results. Hadronic object reconstruction is also one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. Recent refinements to reconstruction and calibration procedures for ATLAS jets and MET result in reduced uncertainties, improved pileup stability and other performance gains. In this contribution, selected highlights of these developments will be presented.