Bump Hunting in Latent Space

Abstract

Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.

Version as published

References in corpus (10)

• ADADELTA: An Adaptive Learning Rate Method
• Classification without labels: Learning from mixed samples in high energy physics
• Extending the Bump Hunt with Machine Learning
• A generic anti-QCD jet tagger
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Cited by in corpus (29)

• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Symmetries, Safety, and Self-Supervision
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Anomalous Jet Identification via Sequence Modeling
• Self-supervised Anomaly Detection for New Physics
• Learning new physics efficiently with nonparametric methods
• Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle $X$ in hadronic final states using $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• What's Anomalous in LHC Jets?
• Lorentz group equivariant autoencoders
• Anomaly Detection under Coordinate Transformations
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
• Anomalies, Representations, and Self-Supervision
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Non-resonant Anomaly Detection with Background Extrapolation
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Unsupervised and lightly supervised learning in particle physics
• Triggering Dark Showers with Conditional Dual Auto-Encoders
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
• Preserving New Physics while Simultaneously Unfolding All Observables
• Detecting New Physics as Novelty -- Complementarity Matters
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
• Graph theory inspired anomaly detection at the LHC