论文标题
高能物理的机器学习校准中的偏差和先验
Bias and Priors in Machine Learning Calibrations for High Energy Physics
论文作者
论文摘要
机器学习提供了一个令人兴奋的机会，可以改善高能物理探测器中几乎所有重建对象的校准。但是，机器学习方法通常取决于训练过程中使用的示例的光谱，这是一个被称为先前依赖性的问题。这是校准的不良属性，需要适用于各种环境。本文的目的是明确强调某些基于机器学习的校准策略的先前依赖性。我们演示了一些基于模拟和基于数据的校准的最新建议如何继承用于训练的样本的特性，这可能会导致下游分析的偏见。在基于仿真的校准的情况下，我们认为我们最近提出的高斯ANSATZ方法可以避免先前依赖性的某些陷阱，而先前独立的基于数据的基于数据仍然是一个开放的问题。
Machine learning offers an exciting opportunity to improve the calibration of nearly all reconstructed objects in high-energy physics detectors. However, machine learning approaches often depend on the spectra of examples used during training, an issue known as prior dependence. This is an undesirable property of a calibration, which needs to be applicable in a variety of environments. The purpose of this paper is to explicitly highlight the prior dependence of some machine learning-based calibration strategies. We demonstrate how some recent proposals for both simulation-based and data-based calibrations inherit properties of the sample used for training, which can result in biases for downstream analyses. In the case of simulation-based calibration, we argue that our recently proposed Gaussian Ansatz approach can avoid some of the pitfalls of prior dependence, whereas prior-independent data-based calibration remains an open problem.
