Speakers
Description
Since the discovery of the Higgs boson, no experimental evidence for physics beyond the Standard Model has been observed at the LHC. While most searches rely on specific signal hypotheses, anomaly detection aims at identifying potential new-physics signatures without assuming a particular BSM model.
In recent years, unsupervised machine learning has become an active area of research in high-energy physics. These techniques learn the properties of Standard Model events directly from data and search for deviations that could indicate the presence of new phenomena.
This contribution reviews recent developments in anomaly detection for fully hadronic final states within the ATLAS Collaboration. Starting from the first fully unsupervised search [1] based on a Variational Recurrent Neural Network trained directly on collision data, we discuss more recent approaches based on Transformer and Graph Neural Network architectures, including EGAT. Their performance is illustrated using the LHC Olympics dataset benchmark [2] and through their first applications to searches for heavy diboson resonances in ATLAS using proton-proton collisions at √s = 13 TeV.
References
[1] Phys. Rev. D 108 (2023) 052009.
[2] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics, Rep. Prog. Phys. 84 (2021) 124201.