# Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment

arXiv: https://arxiv.org/abs/1711.08811 · HTML: https://mlsanity.com/paper/1711.08811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics](https://mlsanity.com/paper/2508.14087/) (2025)

## Abstract (arXiv)

The separation of b-quark initiated jets from those coming from lighter quark flavors (b-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.