43 citations · 89 across the 4 of their papers we have counts for
4 papers
Induced Generative Adversarial Particle Transformers
Anni Li, Venkat Krishnamohan, Raghav Kansal +4
In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC). The message-p…
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Steven Tsan, Raghav Kansal, Anthony Aportela +6
Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at t…
Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya +5
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a wo…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…