# End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data

arXiv: https://arxiv.org/abs/1902.08276 · HTML: https://mlsanity.com/paper/1902.08276/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention](https://mlsanity.com/paper/2106.03898/) (2021)
- [A FAIR and AI-ready Higgs boson decay dataset](https://mlsanity.com/paper/2108.02214/) (2021)
- [Quantum Vision Transformers for Quark-Gluon Classification](https://mlsanity.com/paper/2405.10284/) (2024)
- [End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks](https://mlsanity.com/paper/2204.01681/) (2022)
- [End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data](https://mlsanity.com/paper/2104.14659/) (2021)
- [Graph Generative Models for Fast Detector Simulations in High Energy Physics](https://mlsanity.com/paper/2104.01725/) (2021)
- [Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Images](https://mlsanity.com/paper/2506.14934/) (2025)
- [Quantum Diffusion Model for Quark and Gluon Jet Generation](https://mlsanity.com/paper/2412.21082/) (2024)
- [DeepSNR: A deep learning foundation for offline gravitational wave detection](https://mlsanity.com/paper/2207.04749/) (2022)
- [Uncovering Hidden Systematics in Neural Network Models for High Energy Physics](https://mlsanity.com/paper/2605.07470/) (2026)
- [Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics](https://mlsanity.com/paper/2605.07471/) (2026)

## Abstract (arXiv)

We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial information and demonstrate competitive performance to existing state-of-the-art jet classifiers. We further generalize the end-to-end approach to event-level classification of quark vs. gluon di-jet QCD events. We compare the fully end-to-end approach to using hand-engineered features and demonstrate that the end-to-end algorithm is robust against the effects of underlying event and pile-up.