Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
High Energy Physics - Experiment
2017-11-27 v1 Machine Learning
Abstract
The separation of -quark initiated jets from those coming from lighter quark flavors (-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful -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.
Cite
@article{arxiv.1711.08811,
title = {Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment},
author = {Michela Paganini},
journal= {arXiv preprint arXiv:1711.08811},
year = {2017}
}
Comments
7 pages, 5 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017)