  We explore machine learning-based jet and event identification at the future
Electron-Ion Collider (EIC). We study the effectiveness of machine
learning-based classifiers at relatively low EIC energies, focusing on (i)
identifying the flavor of the jet and (ii) identifying the underlying hard
process of the event. We propose applications of our machine learning-based jet
identification in the key research areas at the future EIC and current
Relativistic Heavy Ion Collider program, including enhancing constraints on
(transverse momentum dependent) parton distribution functions, improving
experimental access to transverse spin asymmetries, studying photon structure,
and quantifying the modification of hadrons and jets in the cold nuclear matter
environment in electron-nucleus collisions. We establish first benchmarks and
contrast the estimated performance of flavor tagging at the EIC with that at
the Large Hadron Collider. We perform studies relevant to aspects of detector
design including particle identification, charge information, and minimum
transverse momentum capabilities. Additionally, we study the impact of using
full event information instead of using only information associated with the
identified jet. These methods can be deployed either on suitably accurate Monte
Carlo event generators, or, for several applications, directly on experimental
data. We provide an outlook for ultimately connecting these machine
learning-based methods with first principles calculations in quantum
chromodynamics.
