Deep Set Auto Encoders for Anomaly Detection in Particle Physics
arXiv:2109.01695 · doi:10.21468/SciPostPhys.12.1.045
Abstract
There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencoder and present results on the Dark Machines Anomaly Score Challenge. We find that the method attains the best anomaly detection ability when there is no decoding step for the network, and the anomaly score is based solely on the representation within the encoded latent space. This method was one of the top-performing models in the Dark Machines Challenge, both for the open data sets as well as the blinded data sets.
v2: change metrics for analyzing models, conclusions unchanged
References in corpus (5)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Extending the Bump Hunt with Machine Learning
- A generic anti-QCD jet tagger
- Transferability of Deep Learning Models in Searches for New Physics at Colliders
- Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
Cited by in corpus (18)
- Anomaly detection in high-energy physics using a quantum autoencoder
- Challenges for Unsupervised Anomaly Detection in Particle Physics
- Online-compatible Unsupervised Non-resonant Anomaly Detection
- Symmetries, Safety, and Self-Supervision
- Learning new physics efficiently with nonparametric methods
- Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
- Anomaly Detection under Coordinate Transformations
- Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
- Anomalies, Representations, and Self-Supervision
- Neural Embedding: Learning the Embedding of the Manifold of Physics Data
- Does Lorentz-symmetric design boost network performance in jet physics?
- Semi-visible jets, energy-based models, and self-supervision
- 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
- Foundations of automatic feature extraction at LHC--point clouds and graphs
- Event Generation and Density Estimation with Surjective Normalizing Flows
- Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure