Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review

2020/07/17 by T. Dorigo, Dorigo, Tommaso, de Castro, Pablo · 1 citation
Computer Science · Decision Sciences · Physics and Astronomy · #Big Data Technologies and Applications #Computational Physics and Python Applications #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2007.09121

openalex publication_date 2020/07/17 · openalex created_date 2020/07/23 · openalex updated_date 2026/07/28

Abstract

In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to include their effect and reduce their impact in the search for optimal selection criteria and variable transformations. The introduction of nuisance parameters complicates the supervised learning task and its correspondence with the data analysis goal, due to their contribution degrading the model performances in real data, and the necessary addition of uncertainties in the resulting statistical inference. The approaches discussed include nuisance-parameterized models, modified or adversary losses, semi-supervised learning approaches, and inference-aware techniques.

Citations

• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• Domain-Adversarial Neural Networks
• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
• Likelihood-free inference with an improved cross-entropy estimator
• Mining gold from implicit models to improve likelihood-free inference

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