Speaker
Alan Kahn
(Columbia University)
Description
We present results of an anomaly detection method using a Variational Recurrent Neural Network trained on the constituent 4-vectors of large-radius jets. By training on a contaminated dataset of largely light QCD jets with some small amount of signal events, we can identify potential new physics objects due to their unique substructure without the need of a pre-determined model hypothesis. We focus on the sequence modeling aspects of this approach, including considerations in pre-processing and sequence ordering of the large-radius jet constituents, and how they affect the performance of the model. We assess the improvement in performance due to these optimizations in the context of the LHC Olympics Black Box datasets from the ML4Jets2020 Workshop in January, 2020.
Primary author
Alan Kahn
(Columbia University)
Co-authors
Daniel Williams
(Columbia University)
Ines Ochoa
(Columbia University)
Julia Gonski
(Columbia University)
