# Jet Flavour Classification Using DeepJet

arXiv: https://arxiv.org/abs/2008.10519 · HTML: https://mlsanity.com/paper/2008.10519/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021)
- [A FAIR and AI-ready Higgs boson decay dataset](https://mlsanity.com/paper/2108.02214/) (2021)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Shared Data and Algorithms for Deep Learning in Fundamental Physics](https://mlsanity.com/paper/2107.00656/) (2021)
- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions](https://mlsanity.com/paper/2309.08630/) (2023)
- [Differentiable Vertex Fitting for Jet Flavour Tagging](https://mlsanity.com/paper/2310.12804/) (2023)
- [Jet Discrimination with Quantum Complete Graph Neural Network](https://mlsanity.com/paper/2403.04990/) (2024)
- [MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks](https://mlsanity.com/paper/2511.01352/) (2025)
- [Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks](https://mlsanity.com/paper/2609.31862/) (2026)
- [Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider](https://mlsanity.com/paper/2105.07530/) (2021)
- [Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey](https://mlsanity.com/paper/2403.11934/) (2024)
- [Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination](https://mlsanity.com/paper/2411.01642/) (2024)
- [B-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture](https://mlsanity.com/paper/2603.21326/) (2026)

## Abstract (arXiv)

Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.