Simulation-Assisted Decorrelation for Resonant Anomaly Detection

Abstract

A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere. One of the prototypical examples for these methods is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum. A significant challenge to methods that rely entirely on data is that they are susceptible to sculpting artificial bumps from the dependence of the machine learning classifier on the invariant mass. We explore two solutions to this challenge by minimally incorporating simulation into the learning. In particular, we study the robustness of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) to correlations between the classifier and the invariant mass. Next, we propose a new approach that only uses the simulation for decorrelation but the Classification without Labels (CWoLa) approach for achieving signal sensitivity. Both methods are compared using a full background fit analysis on simulated data from the LHC Olympics and are robust to correlations in the data.

17 pages, 7 figures

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Cited by in corpus (37)

• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Better Latent Spaces for Better Autoencoders
• Bump Hunting in Latent Space
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Resonant anomaly detection without background sculpting
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• Self-supervised Anomaly Detection for New Physics
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• What's Anomalous in LHC Jets?
• Anomaly Detection under Coordinate Transformations
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Boosting mono-jet searches with model-agnostic machine learning
• A Method to Simultaneously Facilitate All Jet Physics Tasks
• Null Hypothesis Test for Anomaly Detection
• Data-Directed Search for New Physics based on Symmetries of the SM
• Combining Resonant and Tail-based Anomaly Detection
• Resonant Anomaly Detection with Multiple Reference Datasets
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Searching for dark jets with displaced vertices using weakly supervised machine learning
• Bayesian Probabilistic Modelling for Four-Tops at the LHC
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Non-resonant Anomaly Detection with Background Extrapolation
• Deeply Learned Preselection of Higgs Dijet Decays at Future Lepton Colliders
• Simulation-based Anomaly Detection for Multileptons at the LHC
• PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts
• Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Inferring correlated distributions: boosted top jets
• Preserving New Physics while Simultaneously Unfolding All Observables
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
• Strong CWoLa: Binary Classification Without Background Simulation
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC