21-25 March 2022
Academia Sinica
Europe/Zurich timezone

Study for jet flavor tagging by using machine learning

Mar 24, 2022, 11:40 AM
Room 1

Room 1

Oral Presentation Track 1: Physics (including HEP) and Engineering Applications Physics & Engineering


Masahiro Morinaga


In collisions like the Large Hadron Collider (LHC), a large number of physical objects, called jets, are created. They are originated from hadrons such as gluons and quarks, and it is important to identify their origin. For example, a b-jet produced from a bottom quark has features, which can be used for its identification called a “b-tagging” algorithm, enabling precise measurement of the Higgs boson and search for other new particles from the beyond standard model.
Machine learning models have been proposed by various groups to identify jet flavors, but only for specific flavor classification, e.g., classification of the bottom quark and other quarks/gluons (b-tagging), or classification of quarks and gluons (quark and gluon separation). In this study, we propose a method and show results, where we extends the classification to all flavors: b/c/s/d/u/g at once using a modern method based on recent training methods for image recognition models.

Primary authors

Junichi Tanaka (University of Tokyo) Masahiro Morinaga Masahiko Saito (ICEPP, The University of Tokyo) Dr Sanmay Ganguly Tomoe Kishimoto (University of Tokyo)

Presentation materials