Machine Learning on data with sPlot background subtraction

Abstract

Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some machine learning algorithms training due to absence of the lower bound in the loss function. In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all. This allows usage of any out-of-the-box machine learning methods on such data.

References in corpus (1)

• Classification without labels: Learning from mixed samples in high energy physics

Cited by in corpus (5)

• Autoencoders for unsupervised anomaly detection in high energy physics
• A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme
• Observation of a new charmed baryon decaying to ${\Xi }_c^{+}{\pi }^{-}{\pi }^{+}$
• Towards Reliable Neural Generative Modeling of Detectors
• Machine Learning on sWeighted Data