(CERN and EPFL), David Rousseau
(LAL-Orsay, FR), Gian Michele Innocenti
(CERN), Lorenzo Moneta
(CERN), Loukas Gouskos
(CERN), DrPietro Vischia
(Universite Catholique de Louvain (UCL) (BE)), Riccardo Torre
(CERN), Simon Akar
(University of Cincinnati (US))
Anomaly detection at L1 Trigger with Autoencoders25m
We discuss how to adapt and deploy anomaly detection strategies based on Deep Autoenconders on atypical real-time event selection system at the Large Hadron Collider. Considering as a benchmark an inclusive data stream, pre-filtered requiring the presence of one lepton, we discuss different strategies to detect new physics events as anomalies. Using the hls4ml library, we show how resource consumption and latency match the constraints of a typical LHC real-time environment.
We show how to deal with uncertainties on the Reference Model predictions in an agnostic new physics search strategy based on artificial neural networks. Our approach builds directly on the profile likelihood treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.
(Universita e INFN, Padova (IT))
Boosting new physics sensitivity with Variational Autoencoders25m
We show how an anomaly detection algorithm could be integrated in a typical search for new physics in events with jets at the CERN Large Hadron Collider (LHC). We assume that an anomaly detection algorithm is given, trained to identify rare jet types, such as jets originating from the decay of a highly boosted massive particle. We show how this algorithm could be integrated in a search without disrupting the background-estimate strategy while enhancing the sensitivity to new physics. As an example, we consider convolutional variational autoencoders (VAEs) applied to dijet events. The proposed procedure can be generalized to any final state with jets. Once applied to real data, it could contribute to extend the sensitivity of the LHC experiments to previously uncovered new physics scenarios, e.g., broad-resonance and non-resonant jet production from new physics processes.
Kinga Anna Wozniak
(University of Vienna (AT))