Speaker
Description
Jets of collimated particles arising from hard scattered partons have been studied extensively
in hadron collisions. Jets serve a multitude of purposes as they are utilized in fundamental
studies of the Standard Model (SM) and in searches for new particles. Recently, studies of jet
interaction with the quark-gluon plasma (QGP) created in high energy heavy ion collisions are
of growing interest, particularly towards understanding partonic energy loss in the QGP
medium and its related modifications of the jet shower and fragmentation. Since the QGP is a
colored medium, the extent of jet quenching and consequently, the transport properties of the
medium are expected to be sensitive to fundamental properties of the jets such as the flavor of
the parton that initiates the jet. Identifying the jet flavor enables an extraction of the mass
dependence in jet-QGP interactions. We present a novel approach to tagging heavy-flavor jets
at collider experiments utilizing the information contained within jet constituents via the
JetVLAD model architecture. We show the performance of this model as characterized by
common metrics and showcase its ability to extract high purity heavy-flavor jet sample at
various realistic jet momenta and production cross-sections.
