Machine Learning @ ATLAS Experiment


The ATLAS experiment at the Large Hadron Collider (LHC) is looking beyond the Standard Model of particle physics, searching for signs of unknown new physics. An important aspect to be able to find this new physics is the identification of the interesting events within all the events available. Interesting events are called “signal”, while others are “background”. Individuating these signal events, which are indeed extremely rare, is a really challenging task. The LHC has delivered billions of collisions which have been recorded by the ATLAS detector. New physics may be hidden among those.

Traditional techniques do not seem to be enough to uncover this new physics which has proven to be quite elusive. Much more advanced techniques are needed to find it. Machine learning methods are being used for event classification (Neural Networks, Boosted Decision Trees,...) and have been quite useful, decisive for the Higgs boson discovery for instance, but going beyond with deeper Neural Networks is the present and future of the physics analysis at ATLAS and the LHC.