Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review
arXiv:2007.09121
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.
arXiv comment43 pages, 5 figures. v1: original review manuscript. v2: text improvement/fixes from review process
References in corpus (24)
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- The frontier of simulation-based inference
- Combined results of searches for the standard model Higgs boson in pp collisions at sqrt(s) = 7 TeV
- Classification without labels: Learning from mixed samples in high energy physics
- Evidence for a particle produced in association with weak bosons and decaying to a bottom-antibottom quark pair in Higgs boson searches at the Tevatron
- Domain-Adversarial Neural Networks
- Mining gold from implicit models to improve likelihood-free inference
- A generic anti-QCD jet tagger
- Constraining Effective Field Theories with Machine Learning
- What is the Machine Learning?
- Adversarially-trained autoencoders for robust unsupervised new physics searches
- A Guide to Constraining Effective Field Theories with Machine Learning
- Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
- Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
- Nuisance hardened data compression for fast likelihood-free inference
- Automatic physical inference with information maximising neural networks
- Learning to Classify from Impure Samples with High-Dimensional Data
- Learning to Pivot with Adversarial Networks
- DisCo Fever: Robust Networks Through Distance Correlation
- Machine Learning Uncertainties with Adversarial Neural Networks
- INFERNO: Inference-Aware Neural Optimisation
- QBDT, a new boosting decision tree method with systematic uncertainties into training for High Energy Physics
- Likelihood-free inference with an improved cross-entropy estimator
- Practical Statistics for Particle Physicists