Better Latent Spaces for Better Autoencoders

Abstract

Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent space of (variational) autoencoders, specifically in Gaussian mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.

arXiv comment25 pages

References in corpus (31)

• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Dispelling the N^3 myth for the Kt jet-finder
• Anomaly Detection with Density Estimation
• Searching for New Physics with Deep Autoencoders
• Anomaly Detection for Resonant New Physics with Machine Learning
• The Machine Learning Landscape of Top Taggers
• Learning New Physics from a Machine
• Extending the Bump Hunt with Machine Learning
• 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
• Guiding New Physics Searches with Unsupervised Learning
• The Metric Space of Collider Events
• Uncovering latent jet substructure
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• Exploring the Space of Jets with CMS Open Data
• Lund jet images from generative and cycle-consistent adversarial networks
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Unsupervised clustering for collider physics
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Mass Unspecific Supervised Tagging (MUST) for boosted jets
• Transferability of Deep Learning Models in Searches for New Physics at Colliders
• Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders
• Unsupervised Outlier Detection in Heavy-Ion Collisions

Cited by in corpus (31)

• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Anomaly detection in high-energy physics using a quantum autoencoder
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Bump Hunting in Latent Space
• Machine Learning for Anomaly Detection in Particle Physics
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Autoencoders for Semivisible Jet Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Symmetries, Safety, and Self-Supervision
• Unsupervised Hadronic SUEP at the LHC
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• What's Anomalous in LHC Jets?
• Anomaly Detection under Coordinate Transformations
• IRC-safe Graph Autoencoder for unsupervised anomaly detection
• Anomaly detection from mass unspecific jet tagging
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Anomalies, Representations, and Self-Supervision
• Taming modeling uncertainties with Mass Unspecific Supervised Tagging
• Bayesian Probabilistic Modelling for Four-Tops at the LHC
• Semi-visible jets, energy-based models, and self-supervision
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Non-resonant Anomaly Detection with Background Extrapolation
• Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
• 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
• Event Generation and Density Estimation with Surjective Normalizing Flows
• 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