Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows

Abstract

We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

10 + 3 pages, 7 figures

References in corpus (9)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Semi-Supervised Classification with Graph Convolutional Networks
• Extending the Bump Hunt with Machine Learning
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Autoencoders for unsupervised anomaly detection in high energy physics
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow

Cited by in corpus (17)

• Machine Learning for Anomaly Detection in Particle Physics
• CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• 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
• Anomalies, Representations, and Self-Supervision
• Event-based anomaly detection for new physics searches at the LHC using machine learning
• Calorimeter shower superresolution
• End-to-end simulation of particle physics events with Flow Matching and generator Oversampling
• Model selection and signal extraction using Gaussian Process regression
• Semi-visible jets, energy-based models, and self-supervision
• Non-resonant Anomaly Detection with Background Extrapolation
• TopicFlow: Disentangling quark and gluon jets with normalizing flows
• Event Generation and Density Estimation with Surjective Normalizing Flows
• Graph theory inspired anomaly detection at the LHC
• It's not a FAD: first results in using Flows for unsupervised Anomaly Detection at 40 MHz at the Large Hadron Collider