Speaker
Matthew Green
(Adelaide University (AU))
Description
The precision and reach of physics analyses at the LHC is often tied to the performance of hadronic object reconstruction & calibration, with any incremental gains in understanding & reduced uncertainties being impactful on ATLAS results. Recent improvements from machine learning methods include the calibration & pileup tagging or calorimeter clusters, regressions of b-jet and boosted jet calibrations and their performance in data, and the ML-assisted reconstruction of missing transverse momentum.
Authors
Jeff Dandoy
(Carleton University (CA))
Matthew Green
(Adelaide University (AU))
Pierre Antoine Delsart