论文标题
在AI/ML中求解模拟系统
Solving Simulation Systematics in and with AI/ML
论文作者
论文摘要
在使用该系统从实际检测器中推断数据的同时，在模拟数据上训练AI/ML系统会引入一个系统的错误，在许多分析中，它根本不面临。在这种分析中最小化和定量估计不确定性并以与AI/ML技术带来的精度相匹配的精确性和准确性至关重要。在这里，我们强调需要面对这类系统错误，讨论估计它的常规方法，并描述使用本身基于AI/ML的力量的方法来量化和最小化不确定性的方法。我们还描述了将模拟引入AI/ML网络的方法，以允许训练其语义有意义的参数。该白皮书是Snowmass21的计算前沿的贡献。
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.
