Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

Abstract

Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way. In this work, we provide a detailed comparative study between a well-studied unsupervised method called the autoencoder (AE) and a weakly-supervised approach based on the Classification Without Labels (CWoLa) technique. We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search. By construction, the AE classification performance is independent of the amount of injected signal. In contrast, the CWoLa performance improves with increasing signal abundance. When integrating these approaches with a complete background estimate, we find that the two methods have complementary sensitivity. In particular, CWoLa is effective at finding diverse and moderately rare signals while the AE can provide sensitivity to very rare signals, but only with certain topologies. We therefore demonstrate that both techniques are complementary and can be used together for anomaly detection at the LHC.

arXiv comment39 pages, 17 figures

References in corpus (49)

• Adam: A Method for Stochastic Optimization
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Identifying Boosted Objects with N-subjettiness
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• Anomaly Detection with Density Estimation
• Classification without labels: Learning from mixed samples in high energy physics
• Searching for New Physics with Deep Autoencoders
• Anomaly Detection for Resonant New Physics with Machine Learning
• Learning New Physics from a Machine
• Extending the Bump Hunt with Machine Learning
• Search for new resonances in mass distributions of jet pairs using 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• A generic anti-QCD jet tagger
• Search for narrow and broad dijet resonances in proton-proton collisions at $\sqrt{s}=$ 13 TeV and constraints on dark matter mediators and other new particles
• Simulation Assisted Likelihood-free Anomaly Detection
• Novelty Detection Meets Collider Physics
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Guiding New Physics Searches with Unsupervised Learning
• Uncovering latent jet substructure
• Multivariate discrimination and the Higgs + W/Z search
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Global Search for New Physics with 2.0/fb at CDF
• A Living Review of Machine Learning for Particle Physics
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• A robust anomaly finder based on autoencoders
• Bump Hunting in Latent Space
• Model-Independent and Quasi-Model-Independent Search for New Physics at CDF
• Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics
• A general search for new phenomena in ep scattering at HERA
• A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
• A Quasi-Model-Independent Search for New Physics at Large Transverse Momentum
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Unsupervised clustering for collider physics
• A General Search for New Phenomena at HERA
• Mass Unspecific Supervised Tagging (MUST) for boosted jets
• A Quasi-Model-Independent Search for New High p_T Physics at DZero
• Transferability of Deep Learning Models in Searches for New Physics at Colliders
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
• Decoding Dark Matter Substructure without Supervision
• Nonparametric semisupervised classification for signal detection in high energy physics
• Learning Multivariate New Physics
• Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
• Uncovering hidden patterns in collider events with Bayesian probabilistic models

Cited by in corpus (25)

• 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
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Resonant anomaly detection without background sculpting
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Symmetries, Safety, and Self-Supervision
• Quantum Anomaly Detection for Collider Physics
• Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
• Anomaly Detection under Coordinate Transformations
• Anomaly detection from mass unspecific jet tagging
• High-dimensional and Permutation Invariant Anomaly Detection
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Event-based anomaly detection for new physics searches at the LHC using machine learning
• Combining Resonant and Tail-based Anomaly Detection
• Searching for dark jets with displaced vertices using weakly supervised machine learning
• Deep learning techniques for Imaging Air Cherenkov Telescopes
• Non-resonant Anomaly Detection with Background Extrapolation
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Unsupervised and lightly supervised learning in particle physics
• Detecting New Physics as Novelty -- Complementarity Matters
• Preserving New Physics while Simultaneously Unfolding All Observables
• Weakly supervised anomaly detection with event-level variables