Speaker
Mr
Suprio Dubey
(Institute for Theoretical Physics and Mannheim Institute for Intelligent Systems in Medicine, Heidelberg University)
Description
Neural network surrogates for LHC scattering amplitudes require trustworthy uncertainty estimates, a challenging task given the non-Gaussian systematics. We target it using conformal prediction, a distribution-free post-processing to complement trained surrogates with calibrated uncertainties. We find that standard conformal predictions struggle to provide locally calibrated uncertainties. This leads us to introduce FALCON, a novel conformal prediction method that learns locally calibrated confidence intervals. Our simple examples illustrate the power of distribution-free uncertainty quantification for ultra-fast event generation at the LHC.
Authors
Dr
Anja Butter
(LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France, Institut für Theoretische Physik, Universität Heidelberg, Germany)
Henning Bahl
Prof.
Jürgen Hesser
(Mannheim Institute for Intelligent Systems in Medicine, Universität Heidelberg, Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg, Central Institute for Computer Engineering (ZITI), Universität Heidelberg,CZS Heidelberg Initiative for Model-Based AI (MBAI), Universität Heidelberg)
Mr
Suprio Dubey
(Institute for Theoretical Physics and Mannheim Institute for Intelligent Systems in Medicine, Heidelberg University)
Tilman Plehn
(Institut für Theoretische Physik, Universität Heidelberg; Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg)