In particle collisions like the Large Hadron Collider (LHC), a large number of physical objects, called jets, are created.
They originated from hadrons, gluons, or 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 the standard model.
Machine learning models have been proposed by various researchers 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 its results, where we extend the classification to all flavors except top quark: b/c/s/d/u/g at once using a modern method based on a recently developed training strategy for image recognition models.