ATLAS is one of the main experiments at the Large Hadron Collider (LHC), with a broad physics program. It's success however relies on the availability of large amounts of simulated Monte Carlo events. Geant4 provides a detailed and accurate detector simulation, but this simulation is very time consuming, especially in the calorimeters. As a result, many physics analyses will become increasingly limited by the available Monte Carlo statistics in the near future. To solve this problem, sophisticated fast calorimeter simulation tools are developed. These tools employ machine learning algorithms at various levels, either in conjungtion with classical parametrization approaches, or on their own (eg. Generative Adversarial Networks). In this talk, I will describe such tools and demonstrate their importance for future applications in ATLAS. Further powerful machine learning applications used in recent physics analyses will also be discussed.
Bio
Jana Schaarschmidt is an acting assistant professor for experimental high energy physics in the ATLAS group at the University of Washington. ATLAS is one of the largest experiments at the Large Hadron Collider at CERN, it explores the building blocks of the universe and, together with the CMS experiment, discovered the Higgs boson in 2012. Jana completed her Ph.D. in 2010 in Germany, then became a postdoctoral researcher in Orsay, France, where she worked on the Higgs boson decaying to two photons. In 2013 she became a Feinberg Fellow at the Weizmann Institute of Science in Isreal, where she worked mostly on Charged Higgs boson searches. After that she joined the ATLAS group at UW in 2016 and since then focuses on Higgs boson pair production. She has coordinated the ATLAS efforts for searches for Beyond-the-Standard Model Higgs bosons from 2018-2020, and she coordinates the ATLAS Fast Calorimeter Simulation task force since 2016.