Anomaly Detection for Resonant New Physics with Machine Learning

Abstract

Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest 2 sigma excess into a 7 sigma excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.

arXiv commentReplaced with short PRL version. 7 pages, 2 figures. Revised long version will be submitted separately

References in corpus (28)

• 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
• A Brief Introduction to PYTHIA 8.1
• Asymptotic formulae for likelihood-based tests of new physics
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Jet substructure as a new Higgs search channel at the LHC
• Identifying Boosted Objects with N-subjettiness
• sPlot: a statistical tool to unfold data distributions
• Classification without labels: Learning from mixed samples in high energy physics
• WW scattering at the LHC
• A generic anti-QCD jet tagger
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• On hypothesis testing, trials factor, hypertests and the BumpHunter
• Global Search for New Physics with 2.0/fb at CDF
• Model-Independent and Quasi-Model-Independent Search for New Physics at CDF
• Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics
• A general search for new phenomena in ep scattering at HERA
• A Quasi-Model-Independent Search for New Physics at Large Transverse Momentum
• A General Search for New Phenomena at HERA
• uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
• Search for low mass vector resonances decaying to quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Flavor Universal Resonances and Warped Gravity
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Dedicated Strategies for Triboson Signals from Cascade Decays of Vector Resonances
• Detecting a Boosted Diboson Resonance
• Systematic Analysis of High Energy Collider Data

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• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Anomaly Detection with Density Estimation
• Searching for New Physics with Deep Autoencoders
• Energy Flow Networks: Deep Sets for Particle Jets
• Learning New Physics from a Machine
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Simulation Assisted Likelihood-free Anomaly Detection
• Novelty Detection Meets Collider Physics
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• The Metric Space of Collider Events
• QCD-Aware Recursive Neural Networks for Jet Physics
• 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)
• Uncovering latent jet substructure
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Anomaly detection with Convolutional Graph Neural Networks
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
• Better Latent Spaces for Better Autoencoders
• Variational Autoencoders for Anomalous Jet Tagging
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• Machine Learning Uncertainties with Adversarial Neural Networks
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• A robust anomaly finder based on autoencoders
• Bump Hunting in Latent Space
• Exploring the Space of Jets with CMS Open Data
• ABCDisCo: Automating the ABCD Method with Machine Learning
• Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Casting a graph net to catch dark showers
• Learning the latent structure of collider events
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Resonant anomaly detection without background sculpting
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Mapping Machine-Learned Physics into a Human-Readable Space
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• A Theory of Quark vs. Gluon Discrimination
• Mass Unspecific Supervised Tagging (MUST) for boosted jets
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• Unsupervised Hadronic SUEP at the LHC
• Automating the Construction of Jet Observables with Machine Learning
• Mass Agnostic Jet Taggers
• Self-supervised Anomaly Detection for New Physics
• Learning new physics efficiently with nonparametric methods
• Infrared Safety of a Neural-Net Top Tagging Algorithm
• Binary JUNIPR: an interpretable probabilistic model for discrimination
• Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s}=13$ TeV with the ATLAS detector
• SymmetryGAN: Symmetry Discovery with Deep Learning
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• What's Anomalous in LHC Jets?
• Explainable machine learning of the underlying physics of high-energy particle collisions
• Anomaly Detection under Coordinate Transformations
• Anomaly detection from mass unspecific jet tagging
• IRC-safe Graph Autoencoder for unsupervised anomaly detection
• High-dimensional and Permutation Invariant Anomaly Detection
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Searching in CMS Open Data for Dimuon Resonances with Substantial Transverse Momentum
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
• Boosting mono-jet searches with model-agnostic machine learning
• Null Hypothesis Test for Anomaly Detection
• A Method to Simultaneously Facilitate All Jet Physics Tasks
• A semi-supervised approach to dark matter searches in direct detection data with machine learning
• Leveraging universality of jet taggers through transfer learning
• Anomalies, Representations, and Self-Supervision
• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Data-Directed Search for New Physics based on Symmetries of the SM
• GPU coprocessors as a service for deep learning inference in high energy physics
• Enlarging the scope of resonant di-Higgs searches: Hunting for Higgs-to-Higgs cascades in $4b$ final states at the LHC and future colliders
• Anomaly Awareness
• Decoding Dark Matter Substructure without Supervision
• The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
• Combining Resonant and Tail-based Anomaly Detection
• A Data-Directed Paradigm for BSM searches: the bump-hunting example
• Learning Multivariate New Physics
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Learning to Inflate
• The minimal stealth boson: models and benchmarks
• Resonant Anomaly Detection with Multiple Reference Datasets
• Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?
• Jets with electrons from boosted top quarks
• Resonance Searches with Machine Learned Likelihood Ratios
• Class Imbalance Techniques for High Energy Physics
• A Quantum Algorithm for Model-Independent Searches for New Physics
• Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar{t}$ resonance at the LHC
• Model selection and signal extraction using Gaussian Process regression
• Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means
• Deep Learning Jet Substructure from Two-Particle Correlation
• Non-resonant Anomaly Detection with Background Extrapolation
• Weakly-Supervised Anomaly Detection in the Milky Way
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
• Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliders
• Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis
• Variational Autoencoders for Jet Simulation
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates
• Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
• Detecting anomaly in vector boson scattering
• Searching for periodic signals in kinematic distributions using continuous wavelet transforms
• Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
• Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Multiboson signals in the UN2HDM
• New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Exotic diboson $Z^{\prime }$ decays in the U$\mu \nu$SSM
• Topological Learning in Multi-Class Data Sets
• Nonparametric Density Estimation from Markov Chains
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• Similarity-Based Equational Inference in Physics
• OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
• Detecting New Physics as Novelty -- Complementarity Matters
• Preserving New Physics while Simultaneously Unfolding All Observables
• Learning New Physics from an Imperfect Machine
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
• Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features
• Weakly supervised anomaly detection with event-level variables
• Energy Flow in Particle Collisions
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
• Application of Deep Learning to Jet Charge Discrimination