Heavy Flavour Jet Tagging in ATLAS.

The strongly interacting colored partons, i.e., quarks and gluons, never exist in their bare form in nature. During a hard scattering process, instantly after production (time scale orders of 1e-22 seconds), the partons fragment and hadronize to form a spray of color neutral particles which are collectively known as hadrons and mesons. These particles are clustered through an iterative clustering algorithm, and the output cluster is termed as a jet. The jets are macroscopic representatives of the partons.

The jets originating from heavier b and c quarks (heavy-flavor jets) have a structural difference from those originating from light quarks and gluon (light jets). The heavy b/c-hadron (originating from hadronization of b/c-quark) undergoes a secondary decay inside the jet. Thus, a heavy-flavor jet poses a secondary vertex inside the jet, which is missing in the light jets. The geometric features of the secondary vertex, along with track reconstruction parameters, are used to identify heavy-flavor jets and separate them from light jets. This technique is termed heavy-flavor jet tagging or simply flavor-tagging.

Flavor tagging has great importance in LHC physics analyses, especially Higgs boson interaction studies. The Higgs boson has the highest probability to decay into a pair of b-quarks. Many other physics studies related to the standard model dynamics and beyond standard model search experiments use flavor-tagging techniques.

Modern machine learning techniques are heavily used in flavor-tagging to identify b-jets and c-jets from light jets. In recent times, the Weizmann group has played a significant role in improving the flavor-tagging performance, using machine learning, within ATLAS [1, 2]. These advancements allowed for an increase in sensitivity by a factor of 2. The team is continuously improving and developing a more advanced flavor-tagging algorithms within the ATLAS experiment at CERN.