Learning New Physics from an Imperfect Machine

Abstract

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.

References in corpus (13)

• Particle-flow reconstruction and global event description with the CMS detector
• Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at sqrt(s) = 8 TeV
• Anomaly Detection with Density Estimation
• Classification without labels: Learning from mixed samples in high energy physics
• Extending the Bump Hunt with Machine Learning
• A generic anti-QCD jet tagger
• Simulation Assisted Likelihood-free Anomaly Detection
• Model-Independent Jets plus Missing Energy Searches
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Search for a narrow resonance lighter than 200 GeV decaying to a pair of muons in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Parametrized classifiers for optimal EFT sensitivity