Bias and Priors in Machine Learning Calibrations for High Energy Physics. (arXiv:2205.05084v2 [hep-ph] UPDATED)

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 …

arxiv bias energy learning machine machine learning physics