Speaker
Rashmish Mishra
(Harvard University)
Description
The Energy Movers Distance was recently proposed as an advantageous metric to distinguish certain types of signals at the LHC. We explore generalizations of this distance to multiple families of signals and find similar performance anomaly detection through variational autoencoders. We investigate this connection by exploring the correlation of event distances with distances in the latent space of the autoencoder.
| Affiliation | Harvard University |
|---|---|
| Academic Rank | Postdc |
Primary authors
Katherine Fraser
(Harvard University)
Samuel Homiller
(Harvard)
Rashmish Mishra
(Harvard University)
Bryan Ostdiek
(Harvard University)
Matthew Schwartz
