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.

arXiv commentVersion as published

References in corpus (39)

• Scikit-learn: Machine Learning in Python
• TensorFlow: A system for large-scale machine learning
• ADADELTA: An Adaptive Learning Rate Method
• Identifying Boosted Objects with 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
• Extending the Bump Hunt with Machine Learning
• On Empirical Comparisons of Optimizers for Deep Learning
• QCD or What?
• A generic anti-QCD jet tagger
• 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
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Uncovering latent jet substructure
• Autoencoders for unsupervised anomaly detection in high energy physics
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
• Anomaly detection with Convolutional Graph Neural Networks
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
• Better Latent Spaces for Better Autoencoders
• Variational Autoencoders for Anomalous Jet Tagging
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• DisCo Fever: Robust Networks Through Distance Correlation
• Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
• Learning the latent structure of collider events
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Unsupervised clustering for collider physics
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Mass Agnostic Jet Taggers
• Decoding Dark Matter Substructure without Supervision
• Topic Model for four-top at the LHC

Cited by in corpus (30)

• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• 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
• Lorentz group equivariant autoencoders
• 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
• 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
• Unsupervised and lightly supervised learning in particle physics
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Triggering Dark Showers with Conditional Dual Auto-Encoders
• Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Preserving New Physics while Simultaneously Unfolding All Observables
• Detecting New Physics as Novelty -- Complementarity Matters
• Graph theory inspired anomaly detection at the LHC
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure