Jet Classification Using High-Level Features from Anatomy of Top Jets
arXiv:2312.11760 · doi:10.1007/JHEP07(2024)146
Abstract
Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.
36 pages, 8 figures, 12 tables, version published in JHEP. A short discussion on the analysis model and high-level features at sub-hadronic calorimeter angular resolution is added. Code for computing Minkowski functionals is available at: https://github.com/sunghak-lim/mfjet
References in corpus (58)
- 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
- FastJet user manual
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Dispelling the N^3 myth for the Kt jet-finder
- Herwig 7.0 / Herwig++ 3.0 Release Note
- Successive Combination Jet Algorithm For Hadron Collisions
- Better Jet Clustering Algorithms
- Jet substructure as a new Higgs search channel at the LHC
- Event Generation with Sherpa 2.2
- Tuning PYTHIA 8.1: the Monash 2013 Tune
- Identifying Boosted Objects with N-subjettiness
- Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations
- General-purpose event generators for LHC physics
- Top-tagging: A Method for Identifying Boosted Hadronic Tops
- ParticleNet: Jet Tagging via Particle Clouds
- Energy Correlation Functions for Jet Substructure
- Maximizing Boosted Top Identification by Minimizing N-subjettiness
- 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?
- Searching for New Physics with Deep Autoencoders
- Energy Flow Networks: Deep Sets for Particle Jets
- The Machine Learning Landscape of Top Taggers
- Power Counting to Better Jet Observables
- Quark and Gluon Tagging at the LHC
- Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
- Seeing in Color: Jet Superstructure
- Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
- What is the Machine Learning?
- Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
- Energy flow polynomials: A complete linear basis for jet substructure
- Pulling Out All the Tops with Computer Vision and Deep Learning
- The Lund Jet Plane
- QCD-Aware Recursive Neural Networks for Jet Physics
- An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
- Neural Networks for Full Phase-space Reweighting and Parameter Tuning
- Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
- Boosting with Machine Learning
- How Much Information is in a Jet?
- Vincia for Hadron Colliders
- Recursive Neural Networks in Quark/Gluon Tagging
- Measuring Multijet Structure of Hadronic Energy Flow Or What IS A Jet?
- Generative Networks for Precision Enthusiasts
- Tagging a jet from a dark sector with Jet-substructures at colliders
- How to Improve Top Tagging
- Antenna Showers with Hadronic Initial States
- Associated jet and subjet rates in light-quark and gluon jet discrimination
- Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
- DCTRGAN: Improving the Precision of Generative Models with Reweighting
- How to Understand Limitations of Generative Networks
- Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
- Reports of My Demise Are Greatly Exaggerated: -subjettiness Taggers Take On Jet Images
- Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
- Quark-Gluon Jet Differences at LEP
- QCD Jets and Parton Showers
- Exploring the Universality of Hadronic Jet Classification
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- Tagging ultra-boosted jets at FCC-hh using machine learning techniques