Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

Machine Learning Algorithms for b-jet tagging at the ATLAS experiment

Aug 22, 2017, 2:45 PM
107 (Alder Hall)


Alder Hall

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Michela Paganini (Yale University (US))


The separation of b-quark initiated jets from those coming from lighter quark flavours (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 using a multivariate classifier. The potential of modern Machine Learning techniques such as Recurrent Neural Networks and Deep Learning is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.

Primary authors

Salvador Marti I Garcia (IFIC-Valencia (UV/EG-CSIC)) Richard Hawkings (CERN) Andrea Coccaro (University of Geneva) Michela Paganini (Yale University (US))

Presentation materials

Peer reviewing