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 (arXiv:2312.11618) and particle incident energy calibration (arXiv:2404.18992) without any additional training costs. Using CaloFlow as an example, we show that the unsupervised anomaly detector is sensitive to a wide range of signals, while the calibration approach is prior-independent and has access to per-shower resolution information.
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
