13–17 May 2024
University of Pittsburgh / Carnegie Mellon University
US/Eastern timezone

The versatility of flow-based fast calorimeter surrogate models

14 May 2024, 14:45
15m
Barco Law Building 109 (University of Pittsburgh)

Barco Law Building 109

University of Pittsburgh

Machine Learning & AI Machine Learning & AI

Speaker

Ian Pang

Description

Normalizing flows have proven to be state-of-the-art for fast calorimeter simulation. With access to the likelihood, these flow-based fast calorimeter surrogate models can be used for other tasks such as unsupervised anomaly detection and incident energy regression without any additional training costs.

Authors

Ben Nachman (Lawrence Berkeley National Lab. (US)) Dr Claudius Krause (HEPHY Vienna (ÖAW)) David Shih Haoxing Du Ian Pang Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US)) Yunhao Zhu

Presentation materials