Speakers
Description
Searches for physics beyond the Standard Model at the LHC are traditionally optimized for specific signal hypotheses. Anomaly detection provides a complementary approach by identifying events that deviate from the expected Standard Model background without relying on a particular new-physics model. In fully hadronic final states, these techniques exploit the rich information encoded in the internal structure of hadronic jets, learning directly from collision data or background-dominated samples.
This contribution presents the current status of anomaly detection studies in fully hadronic final states within the ATLAS Collaboration. The first fully unsupervised search [1], based on a Variational Recurrent Neural Network trained directly on recorded data, is presented together with more recent developments based on Transformer architectures and Graph Neural Networks (EGAT and GIN). Particular attention is given to the different representations of jet constituents used by these models and their impact on the identification of anomalous events.
Results obtained with the LHC Olympics benchmark dataset [2] are presented together with the first applications of these techniques to searches for high-mass diboson resonances in proton-proton collisions at √s = 13 TeV recorded by the ATLAS detector. The talk will summarize the current status of these studies and discuss their prospects for future model-independent searches at the LHC.
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.