Guiding New Physics Searches with Unsupervised Learning

Abstract

We propose a new scientific application of unsupervised learning techniques to boost our ability to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. We build a statistical test upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a proof-of-concept, we apply our method to synthetic Gaussian data, and to a simulated dark matter signal at the Large Hadron Collider. Even in the case where the background can not be simulated accurately enough to claim discovery, the technique is a powerful tool to identify regions of interest for further study.

25 pages, 7 figures. Updated with new treatment of uncertainties and to match published version

References in corpus (13)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Classification without labels: Learning from mixed samples in high energy physics
• Learning New Physics from a Machine
• Mining gold from implicit models to improve likelihood-free inference
• A Guide to Constraining Effective Field Theories with Machine Learning
• On hypothesis testing, trials factor, hypertests and the BumpHunter
• Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering
• A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
• Recommendations of the LHC Dark Matter Working Group: Comparing LHC searches for heavy mediators of dark matter production in visible and invisible decay channels
• Digging Deeper for New Physics in the LHC Data
• Machine learning and multivariate goodness of fit
• Accelerating the BSM interpretation of LHC data with machine learning

Cited by in corpus (74)

• Discovering physical concepts with neural networks
• Anomaly Detection with Density Estimation
• Searching for New Physics with Deep Autoencoders
• Simulation Assisted Likelihood-free Anomaly Detection
• Novelty Detection Meets Collider Physics
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• Machine and Deep Learning Applications in Particle Physics
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• The Metric Space of Collider Events
• 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)
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Anomaly detection with Convolutional Graph Neural Networks
• Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
• Better Latent Spaces for Better Autoencoders
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• Exploring the Space of Jets with CMS Open Data
• ABCDisCo: Automating the ABCD Method with Machine Learning
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Learning the latent structure of collider events
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• INFERNO: Inference-Aware Neural Optimisation
• CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Unsupervised Hadronic SUEP at the LHC
• Exploring anomalous couplings in Higgs boson pair production through shape analysis
• Learning new physics efficiently with nonparametric methods
• Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s}=13$ TeV with the ATLAS detector
• Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review
• What's Anomalous in LHC Jets?
• Operationally meaningful representations of physical systems in neural networks
• Anomaly Detection under Coordinate Transformations
• IRC-safe Graph Autoencoder for unsupervised anomaly detection
• Anomaly detection from mass unspecific jet tagging
• Using a nested anomaly detection machine learning algorithm to study the neutral triple gauge couplings at an \texorpdfstring{$e^{+}{e}^{-}$}{e+e-} collider
• Detecting anomalous quartic gauge couplings using the isolation forest machine learning algorithm
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Boosting mono-jet searches with model-agnostic machine learning
• Invisible Higgs search through Vector Boson Fusion: A deep learning approach
• Leveraging universality of jet taggers through transfer learning
• Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra
• Decoding Dark Matter Substructure without Supervision
• Anomaly Awareness
• MLAnalysis: An open-source program for high energy physics analyses
• The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
• Learning Multivariate New Physics
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Accelerating the search for Axion-Like Particles with machine learning
• A Quantum Algorithm for Model-Independent Searches for New Physics
• Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder
• Non-resonant Anomaly Detection with Background Extrapolation
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Optimize the event selection strategy to study the anomalous quartic gauge couplings at muon colliders using the support vector machine and quantum support vector machine
• Unsupervised and lightly supervised learning in particle physics
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
• Detecting anomaly in vector boson scattering
• Searching for periodic signals in kinematic distributions using continuous wavelet transforms
• Reconstructing axion-like particles from beam dumps with simulation-based inference
• Search for anomalous quartic gauge couplings in the process ${\mu }^{+}{\mu }^{-}\to \bar{\nu }\nu \gamma \gamma$ with a nested local outlier factor
• Detecting New Physics as Novelty -- Complementarity Matters
• Finding excesses in model parameter space
• Learning New Physics from an Imperfect Machine
• A quantum machine learning classifier to search for new physics
• Preserving New Physics while Simultaneously Unfolding All Observables
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
• Noisy Labels for Weakly Supervised Gamma Hadron Classification