Investigation of Autoencoders for Jet Images in Particle Physics

Abstract: Dark matter is an invisible type of matter believed to make up 85 % of the matter in the universe, but it has not yet been identified by experiments. According to certain particle physics theories, possible signatures of dark matter are so-called dark jets. They are the dark matter equivalent to classic jets, i.e collimated streams of particles. These jets could be produced from the proton-proton collisions at the Large Hadron Collider (LHC). The ATLAS experiment at the LHC is currently searching for such jets. There are two challenges in discovering dark jets. Firstly, the traces left in the detector by dark jets are not well understood, so the best lead is to search for something anomalous. Secondly, current data storage limitations force us to discard data and this reduces the probability to register a dark jet. Using machine learning techniques -- or more specifically autoencoders -- is a proposed solution as autoencoders can perform compression and anomaly detection simultaneously. This master's thesis investigates the use of autoencoders for jet images, a two-dimensional jet representation. A group of different autoencoders are trained separately to learn inherent structures in QCD jets (background, ordinary events). They are then used to recognize boosted W-boson jets (signal, anomalous events) with a different signature. The separation of boosted W-boson jets and QCD jets is a simplified version of the problem of separating possible dark jets from QCD jets. The autoencoders were able to compress the background jet images threefold with an error of less than 5 % for over 95 % of the data. However, for signal jet images the error was found to be even smaller. This made anomaly detection impossible since the opposite is required for the method to work. The difference between signal and background could be too small for the simple autoencoders to distinguish.