Extending the Bump Hunt with Machine Learning

Abstract

The oldest and most robust technique to search for new particles is to look for `bumps' in invariant mass spectra over smoothly falling backgrounds. We present a new extension of the bump hunt that naturally benefits from modern machine learning algorithms while remaining model-agnostic. This approach is based on the Classification Without Labels (CWoLa) method where the invariant mass is used to create two potentially mixed samples, one with little or no signal and one with a potential resonance. Additional features that are uncorrelated with the invariant mass can be used for training the classifier. Given the lack of new physics signals at the Large Hadron Collider (LHC), such model-agnostic approaches are critical for ensuring full coverage to fully exploit the rich datasets from the LHC experiments. In addition to illustrating how the new method works in simple test cases, we demonstrate the power of the extended bump hunt on a realistic all-hadronic resonance search in a channel that would not be covered with existing techniques.

Longer, explanatory companion to arXiv:1805.02664. 35 pages, 14 figures

References in corpus (73)

• 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
• TensorFlow: A system for large-scale machine learning
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• Asymptotic formulae for likelihood-based tests of new physics
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• sPlot: a statistical tool to unfold data distributions
• Soft Drop
• Identification of b-quark jets with the CMS experiment
• Trial factors for the look elsewhere effect in high energy physics
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• Classification without labels: Learning from mixed samples in high energy physics
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Search for new phenomena in dijet events using 37 fb${}^{-1}$ of $pp$ collision data collected at $\sqrt{s}=$13 TeV with the ATLAS detector
• Deep-learned Top Tagging with a Lorentz Layer
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Search for narrow resonances decaying to dijets in proton-proton collisions at sqrt(s) = 13 TeV
• A generic anti-QCD jet tagger
• Search for dijet resonances in proton-proton collisions at sqrt(s) = 13 TeV and constraints on dark matter and other models
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Pulling Out All the Tops with Computer Vision and Deep Learning
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Light-quark and gluon jet discrimination in pp collisions at $\sqrt{s}$ = 7 TeV with the ATLAS detector
• Identification techniques for highly boosted W bosons that decay into hadrons
• Learning to Classify from Impure Samples with High-Dimensional Data
• Search for pair-produced resonances decaying to jet pairs in proton-proton collisions at sqrt(s) = 8 TeV
• Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Inclusive search for a highly boosted Higgs boson decaying to a bottom quark-antiquark pair
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• Weakly Supervised Classification in High Energy Physics
• How Much Information is in a Jet?
• Global Search for New Physics with 2.0/fb at CDF
• Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at $\sqrt{s}=8$ TeV
• RECAST: Extending the Impact of Existing Analyses
• Novel Jet Observables from Machine Learning
• A search for pair-produced resonances in four-jet final states at $\sqrt{s}$ =13 TeV with the ATLAS detector
• Jet Charge and Machine Learning
• 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
• (Machine) Learning to Do More with Less
• Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Search for pair-produced massive coloured scalars in four-jet final states with the ATLAS detector in proton-proton collisions at sqrt(s) = 7 TeV
• A general search for new phenomena in ep scattering at HERA
• Search for diboson resonances with boson-tagged jets in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• A Quasi-Model-Independent Search for New Physics at Large Transverse Momentum
• Search for massive resonances decaying into WW, WZ, ZZ, qW, and qZ with dijet final states at $\sqrt{s}=$ 13 TeV
• Search for a massive resonance decaying to a pair of Higgs bosons in the four b quark final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Search for pair-produced dijet resonances in four-jet final states in pp collisions at sqrt(s) = 7 TeV
• A General Search for New Phenomena at HERA
• Search for heavy resonances decaying to a $W$ or $Z$ boson and a Higgs boson in the $q{\bar{q}}^{(\prime )}b\bar{b}$ final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Search for massive resonances decaying into WW, WZ or ZZ bosons in proton-proton collisions at sqrt(s) = 13 TeV
• Search for t t-bar resonances in highly boosted lepton+jets and fully hadronic final states in proton-proton collisions at sqrt(s) = 13 TeV
• Search for low mass vector resonances decaying to quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Search for resonances in the mass distribution of jet pairs with one or two jets identified as $b$-jets in proton--proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Estimating the significance of a signal in a multi-dimensional search
• Flavor Universal Resonances and Warped Gravity
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Identification of high transverse momentum top quarks in $pp$ collisions at $\sqrt{s}$ = 8 TeV with the ATLAS detector
• Search for $W^{\prime }\to tb$ decays in the hadronic final state using pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Stealth multiboson signals
• Beam-induced and cosmic-ray backgrounds observed in the ATLAS detector during the LHC 2012 proton-proton running period
• A search for top squarks with R-parity-violating decays to all-hadronic final states with the ATLAS detector in $\sqrt{s}$ = 8 TeV proton-proton collisions
• Detecting a Boosted Diboson Resonance
• Search for high-mass Z gamma resonances in proton-proton collisions at sqrt(s) = 8 and 13 TeV using jet substructure techniques
• Systematic Analysis of High Energy Collider Data
• Running bumps from stealth bosons

Cited by in corpus (102)

• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Anomaly Detection with Density Estimation
• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• QCD or What?
• Simulation Assisted Likelihood-free Anomaly Detection
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• The Metric Space of Collider Events
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Uncovering latent jet substructure
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• Better Latent Spaces for Better Autoencoders
• Variational Autoencoders for Anomalous Jet Tagging
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• 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
• Machine Learning for Anomaly Detection in Particle Physics
• 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
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Resonant anomaly detection without background sculpting
• Unsupervised clustering for collider physics
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• A Theory of Quark vs. Gluon Discrimination
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Mass Unspecific Supervised Tagging (MUST) for boosted jets
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
• 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
• Automating the Construction of Jet Observables with Machine Learning
• Jet Substructure from Dark Sector Showers
• Mass Agnostic Jet Taggers
• Learning new physics efficiently with nonparametric methods
• Self-supervised Anomaly Detection for New Physics
• Detecting an axion-like particle with machine learning at the LHC
• SymmetryGAN: Symmetry Discovery with Deep Learning
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• What's Anomalous in LHC Jets?
• Anomaly Detection under Coordinate Transformations
• Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV
• High-dimensional and Permutation Invariant Anomaly Detection
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
• Boosting mono-jet searches with model-agnostic machine learning
• A Method to Simultaneously Facilitate All Jet Physics Tasks
• Solving Key Challenges in Collider Physics with Foundation Models
• 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
• GPU coprocessors as a service for deep learning inference in high energy physics
• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Data-Directed Search for New Physics based on Symmetries of the SM
• On the BSM reach of four top production at the LHC
• Combining Resonant and Tail-based Anomaly Detection
• A Data-Directed Paradigm for BSM searches: the bump-hunting example
• The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
• Learning Multivariate New Physics
• Tagging boosted hadronic objects with dynamical grooming
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Resonant Anomaly Detection with Multiple Reference Datasets
• Jet tagging made easy
• Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?
• Resonance Searches with Machine Learned Likelihood Ratios
• A Quantum Algorithm for Model-Independent Searches for New Physics
• Searching for dark jets with displaced vertices using weakly supervised machine learning
• Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar{t}$ resonance at the LHC
• Class Imbalance Techniques for High Energy Physics
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Non-resonant Anomaly Detection with Background Extrapolation
• Weakly-Supervised Anomaly Detection in the Milky Way
• Simulation-based Anomaly Detection for Multileptons at the LHC
• Unsupervised and lightly supervised learning in particle physics
• 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
• Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
• Reporting Results in High Energy Physics Publications: a Manifesto
• Signal-background interference for digluon resonances at the Large Hadron Collider
• 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
• Exotic diboson $Z^{\prime }$ decays in the U$\mu \nu$SSM
• New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
• Learning New Physics from an Imperfect Machine
• OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
• Preserving New Physics while Simultaneously Unfolding All Observables
• Detecting New Physics as Novelty -- Complementarity Matters
• Energy Flow in Particle Collisions
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
• Weakly supervised anomaly detection with event-level variables
• Graph theory inspired anomaly detection at the LHC