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.

中文翻译：

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高能物理机器学习校准中的偏差和先验

机器学习提供了一个令人兴奋的机会来改进高能物理探测器中几乎所有重建物体的校准。然而，机器学习方法通​​常依赖于训练期间使用的示例谱，这个问题被称为先验依赖。这是校准的不良特性，需要适用于各种环境。本文的目的是明确强调一些基于机器学习的校准策略的先验依赖性。我们展示了一些最近关于基于模拟和基于数据的校准的提议如何继承用于训练的样本的属性，这可能导致下游分析的偏差。在基于模拟的校准的情况下，