Anomaly detection with flow-based fast calorimeter simulators

Abstract

Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The CaloFlow model is designed to generate single photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being unlikely. As expected, the tagger struggles when the signal photons are nearly collinear, but is otherwise effective. This approach is complementary to a supervised classifier trained on only specific signal models using the same low-level calorimeter inputs. While the supervised classifier is also highly effective at unseen signal models, the unsupervised method is more sensitive in certain regions and thus we expect that the ultimate performance will require a combination of these approaches.

14 pages, 8 figures

References in corpus (48)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Anomaly Detection with Density Estimation
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Searching for New Physics with Deep Autoencoders
• Event Generation with Normalizing Flows
• QCD or What?
• Novelty Detection Meets Collider Physics
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• AtlFast3: the next generation of fast simulation in ATLAS
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• i-flow: High-dimensional Integration and Sampling with Normalizing Flows
• Score-based Generative Models for Calorimeter Shower Simulation
• Exploring phase space with Neural Importance Sampling
• Invertible Networks or Partons to Detector and Back Again
• Fast and accurate simulation of particle detectors using generative adversarial networks
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• Understanding Event-Generation Networks via Uncertainties
• Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
• Generative Networks for Precision Enthusiasts
• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Targeting Multi-Loop Integrals with Neural Networks
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Resonant anomaly detection without background sculpting
• Generative Models for Fast Calorimeter Simulation.LHCb case
• MadNIS -- Neural Multi-Channel Importance Sampling
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• Two Invertible Networks for the Matrix Element Method
• Measuring QCD Splittings with Invertible Networks
• Inference of cosmic-ray source properties by conditional invertible neural networks
• ν-Flows: Conditional Neutrino Regression
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Returning CP-Observables to The Frames They Belong
• Anomaly Detection under Coordinate Transformations
• CaloFlow for CaloChallenge Dataset 1
• Perfect density models cannot guarantee anomaly detection
• High-dimensional and Permutation Invariant Anomaly Detection
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
• ${\nu }^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
• Calorimeter shower superresolution
• Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Cited by in corpus (6)

• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Unifying Simulation and Inference with Normalizing Flows
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• It's not a FAD: first results in using Flows for unsupervised Anomaly Detection at 40 MHz at the Large Hadron Collider