Unsupervised clustering for collider physics

Abstract

We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar properties. We show how this method can be applied to an unsupervised multiclass classification as well as for anomaly detection, which can be used for new physics searches.

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• An Introduction to PYTHIA 8.2
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• Extending the Bump Hunt with Machine Learning
• GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
• Search for new phenomena with large jet multiplicities and missing transverse momentum using large-radius jets and flavour-tagging at ATLAS in 13 TeV $pp$ collisions
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Cited by in corpus (23)

• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Anomaly detection with Convolutional Graph Neural Networks
• Better Latent Spaces for Better Autoencoders
• Bump Hunting in Latent Space
• Machine Learning for Anomaly Detection in Particle Physics
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• 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
• Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
• Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
• Non-resonant Anomaly Detection with Background Extrapolation
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning
• Preserving New Physics while Simultaneously Unfolding All Observables
• Detecting New Physics as Novelty -- Complementarity Matters
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure