Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
arXiv:2401.08777
Abstract
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection for high-energy physics. First, rather than train a generative model on the single most dominant background process, we build detection algorithms using representation learning from multiple background types, thus taking advantage of more information to improve estimation of what is relevant for detection. Second, we generalize decorrelation to the multi-background setting, thus directly enforcing a more complete definition of robustness for anomaly detection. We demonstrate the benefit of the proposed robust multi-background anomaly detection algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.
References in corpus (23)
- Auto-Encoding Variational Bayes
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
- Evidence for oscillation of atmospheric neutrinos
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Equality of Opportunity in Supervised Learning
- NICE: Non-linear Independent Components Estimation
- R-parity violating supersymmetry
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Observation of a new boson with mass near 125 GeV in pp collisions at sqrt(s) = 7 and 8 TeV
- Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm
- Searching for New Physics with Deep Autoencoders
- Scaling Out-of-Distribution Detection for Real-World Settings
- Dijet resonance search with weak supervision using TeV collisions in the ATLAS detector
- Classifying Anomalies THrough Outer Density Estimation (CATHODE)
- DisCo Fever: Robust Networks Through Distance Correlation
- Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
- Autoencoders for Semivisible Jet Detection
- Self-supervised Anomaly Detection for New Physics
- Search for resonant and nonresonant production of pairs of dijet resonances in proton-proton collisions at = 13 TeV
- Neural Embedding: Learning the Embedding of the Manifold of Physics Data
- Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
- Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations