Learning new physics efficiently with nonparametric methods

Abstract

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.

22 pages, 13 figures

References in corpus (9)

• Extending the Bump Hunt with Machine Learning
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Autoencoders for unsupervised anomaly detection in high energy physics
• Bump Hunting in Latent Space
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• MUSiC: a model-unspecific search for new physics in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Digging Deeper for New Physics in the LHC Data
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions

Cited by in corpus (13)

• CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
• Anomaly Detection under Coordinate Transformations
• Unbinned multivariate observables for global SMEFT analyses from machine learning
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Null Hypothesis Test for Anomaly Detection
• Resonant Anomaly Detection with Multiple Reference Datasets
• Goodness of fit by Neyman-Pearson testing
• Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
• Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder
• Non-resonant Anomaly Detection with Background Extrapolation
• Search for anomalous quartic gauge couplings in the process ${\mu }^{+}{\mu }^{-}\to \bar{\nu }\nu \gamma \gamma$ with a nested local outlier factor
• A quantum machine learning classifier to search for new physics
• Anomaly preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC