Web: http://arxiv.org/abs/2205.05084

May 12, 2022, 1:10 a.m. | Rikab Gambhir, Benjamin Nachman, Jesse Thaler

stat.ML updates on arXiv.org arxiv.org

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

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