Solving Simulation Systematics in and with AI/ML

Autor:	Viren, Brett, Huang, Jin, Huang, Yi, Lin, Meifeng, Ren, Yihui, Terao, Kazuhiro, Torbunov, Dmitrii, Yu, Haiwang
Rok vydání:	2022
Předmět:	High Energy Physics - Experiment Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many analyses is simply not confronted. It is crucial to minimize and to quantitatively estimate the uncertainties in such analysis and do so with a precision and accuracy that matches those that AI/ML techniques bring. Here we highlight the need to confront this class of systematic error, discuss conventional ways to estimate it and describe ways to quantify and to minimize the uncertainty using methods which are themselves based on the power of AI/ML. We also describe methods to introduce a simulation into an AI/ML network to allow for training of its semantically meaningful parameters. This whitepaper is a contribution to the Computational Frontier of Snowmass21. Comment: This whitepaper is a contribution to the Computational Frontier of Snowmass21
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.06112 Zobrazit plný text záznamu View this record from Arxiv