Speaker
Description
The main goal for the upcoming LHC runs is still to discover BSM physics. It will require analyses able to probe regions not linked to specific models but generally identified as beyond the Standard Model. Autoencoders are the ideal analysis tool for this type of search. Energy-based machine learning models have been shown to be flexible and powerful models to describe high-dimensional feature space and combine out-of-distribution searches with density estimation. I will present a Normalized Autoencoder, the first proper and high-performance anomaly search algorithm for LHC jets. I will apply it to jets images and show how the NAE reliably identifies anomalous jets symmetrically in the directions of higher and lower complexity. I will show that NAE works well for top vs QCD jet, as well as for the more challenging dark jet signals.
