Speaker
Description
This work presents a strategy for model-independent searches for new physics using deep unsupervised learning techniques at the LHC. The first study applies a deep autoencoder to 140fb^-1 of sqrt(s)=13TeV pp collision data recorded by the ATLAS detector, selecting outlier events that deviate from the dominant Standard Model kinematics. Invariant mass distribuations of various two-body systems - such as jet+jet, jet+lepton, and b-jet+photon - are analyzed in these anomalous regions to search for resonant structures, setting 95% CL upper limits on BSM cross sections. Complementing this, a second study introduces ADFilter, a public web-based tool enabling theorists and experimentalists to evaluate BSM model sensitivity using trained autoencoders. ADFilter processes event-level data through a standardized pipeline - including rapidity-mass matrix construction, anomaly scoring, and cross-section evaluation - allowing rapid esimation of anomaly region acceptances and facilitating reinterpretation of published ATLAS results.
