High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions

Abstract

Experiments at a future $e^{+}{e}^{-}$ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in $e^{+}{e}^{-}$ collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.

24 pages, 13 figures

References in corpus (23)

• 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
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Classification without labels: Learning from mixed samples in high energy physics
• Extending the Bump Hunt with Machine Learning
• A generic anti-QCD jet tagger
• ILC Operating Scenarios
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Autoencoders for unsupervised anomaly detection in high energy physics
• Anomaly detection with Convolutional Graph Neural Networks
• A Living Review of Machine Learning for Particle Physics
• Better Latent Spaces for Better Autoencoders
• Bump Hunting in Latent Space
• Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
• Narrow resonances studies with the radiative return method
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning
• Transferability of Deep Learning Models in Searches for New Physics at Colliders
• Anomalous Jet Identification via Sequence Modeling
• Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
• Initial State Radiation: A success story
• The Radiative Return: A Review of Experimental Results

Cited by in corpus (4)

• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Learning new physics efficiently with nonparametric methods
• Semi-supervised permutation invariant particle-level anomaly detection
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure