Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
This work addresses a fundamental tool for particle physics analysis at the ATLAS experiment, but appears incremental as it compares existing methods without claiming major breakthroughs.
The paper tackled the problem of distinguishing b-quark jets from lighter quark jets (b-tagging) for the ATLAS experiment by exploring modern deep learning techniques on simulated data, comparing them to traditional classifiers like boosted decision trees, but did not report specific numerical results.
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.