Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
arXiv:1807.03312 · doi:10.1007/JHEP10(2018)181
Abstract
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays. By taking the Higgs jets and QCD jets as examples, we show that the ANN of the angular spectrum input has similar performance to existing taggers. In addition, some improvement is seen when additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multi-point correlations in the jet.
arXiv comment18 pages, 12 figures, published in JHEP. A cut-based analysis is added
References in corpus (36)
- 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
- An Introduction to PYTHIA 8.2
- FastJet user manual
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Parton distributions with LHC data
- Successive Combination Jet Algorithm For Hadron Collisions
- Better Jet Clustering Algorithms
- Jet substructure as a new Higgs search channel at the LHC
- Identifying Boosted Objects with N-subjettiness
- Soft Drop
- Jet Trimming
- Towards an understanding of jet substructure
- Top-tagging: A Method for Identifying Boosted Hadronic Tops
- Energy Correlation Functions for Jet Substructure
- Fat Jets for a Light Higgs
- Stop Reconstruction with Tagged Tops
- Deep learning in color: towards automated quark/gluon jet discrimination
- Jet-Images -- Deep Learning Edition
- Deep-learning Top Taggers or The End of QCD?
- Strategies to Identify Boosted Tops
- Power Counting to Better Jet Observables
- Quark and Gluon Tagging at the LHC
- Seeing in Color: Jet Superstructure
- New Angles on Energy Correlation Functions
- Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
- A generic anti-QCD jet tagger
- Energy flow polynomials: A complete linear basis for jet substructure
- How Much Information is in a Jet?
- Jet Substructure Without Trees
- Recursive Neural Networks in Quark/Gluon Tagging
- Measuring Multijet Structure of Hadronic Energy Flow Or What IS A Jet?
- Novel Jet Observables from Machine Learning
- Quark-gluon discrimination in the search for gluino pair production at the LHC
- Angular Scaling in Jets
- A Framework for Finding Anomalous Objects at the LHC
Cited by in corpus (21)
- Searching for New Physics with Deep Autoencoders
- QCD-Aware Recursive Neural Networks for Jet Physics
- Exploring the Space of Jets with CMS Open Data
- Quark-Gluon Tagging: Machine Learning vs Detector
- A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
- Deep-Learning Jets with Uncertainties and More
- Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
- Per-Object Systematics using Deep-Learned Calibration
- Mass Agnostic Jet Taggers
- Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
- Probing triple Higgs coupling with machine learning at the LHC
- Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
- CapsNets Continuing the Convolutional Quest
- Invisible Higgs search through Vector Boson Fusion: A deep learning approach
- Leveraging universality of jet taggers through transfer learning
- Deep Learning Jet Substructure from Two-Particle Correlation
- Jet Classification Using High-Level Features from Anatomy of Top Jets
- Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
- Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation
- Riemannian Data preprocessing in Machine Learning to focus on QCD color structure
- Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features