Mass Agnostic Jet Taggers

Abstract

Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.

arXiv comment25 pages, 15 figures, plus appendices. Prepared for submission to SciPost Physics

References in corpus (40)

• 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
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Identifying Boosted Objects with N-subjettiness
• N-Jettiness: An Inclusive Event Shape to Veto Jets
• Deep Learning and its Application to LHC Physics
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Techniques for improved heavy particle searches with jet substructure
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Anomaly Detection for Resonant New Physics with Machine Learning
• The Machine Learning Landscape of Top Taggers
• Parameterized Machine Learning for High-Energy Physics
• Extending the Bump Hunt with Machine Learning
• Deep-learned Top Tagging with a Lorentz Layer
• QCD or What?
• A generic anti-QCD jet tagger
• What is the Machine Learning?
• Pulling Out All the Tops with Computer Vision and Deep Learning
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Learning to Classify from Impure Samples with High-Dimensional Data
• Boosting $H\to b\bar{b}$ with Machine Learning
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• How Much Information is in a Jet?
• Weakly Supervised Classification in High Energy Physics
• Jet Charge and Machine Learning
• (Machine) Learning to Do More with Less
• LHC Signals from Cascade Decays of Warped Vector Resonances
• uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
• Flavor Universal Resonances and Warped Gravity
• Automating the Construction of Jet Observables with Machine Learning
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Infrared Safety of a Neural-Net Top Tagging Algorithm
• Dedicated Strategies for Triboson Signals from Cascade Decays of Vector Resonances
• Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
• Stealth multiboson signals
• Identification of High-Momentum Top Quarks, Higgs Bosons, and W and Z Bosons Using Boosted Event Shapes
• Detecting a Boosted Diboson Resonance
• New approaches for boosting to uniformity

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• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Variational Autoencoders for Anomalous Jet Tagging
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• DisCo Fever: Robust Networks Through Distance Correlation
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Bump Hunting in Latent Space
• ABCDisCo: Automating the ABCD Method with Machine Learning
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Uncertainty Aware Learning for High Energy Physics
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Equivariant Energy Flow Networks for Jet Tagging
• Mass Unspecific Supervised Tagging (MUST) for boosted jets
• Jet Substructure from Dark Sector Showers
• A Cautionary Tale of Decorrelating Theory Uncertainties
• A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer
• Dynamic Radius Jet Clustering Algorithm
• Enhancing searches for resonances with machine learning and moment decomposition
• Jet tagging made easy
• Bias and Priors in Machine Learning Calibrations for High Energy Physics
• Meta-learning and data augmentation for mass-generalised jet taggers
• Deeply Learned Preselection of Higgs Dijet Decays at Future Lepton Colliders
• Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
• Riemannian Data preprocessing in Machine Learning to focus on QCD color structure
• PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts
• Designing Observables for Measurements with Deep Learning
• Heavy Particle Jet Identification with Zest