Current Position: Postdoctoral Researcher
Current Institution: MIT
Thesis Title: “Novel Uses of Machine Learning for Differential Jet Quenching Measurements at the LHC”
Thesis Abstract: At sufficiently high temperatures and pressures, QCD matter becomes a hot and dense deconfined medium known as the Quark Gluon Plasma (QGP). Collisions of relativistic heavy-ions are used to recreate the QGP, providing a rich laboratory for exploring the mysteries of the strong interaction. The intrinsic and dynamic properties of the QGP are probed with jets, narrow cones of particles resulting from the scattering of quarks and gluons with a high momentum transfer. In heavy-ion collisions, jets interact with the QGP as they traverse it, leading to jet energy loss and modification of the jet’s internal structure. The ALICE detector at the LHC is optimized for measurements in the heavy-ion collision environment and allows for the reconstruction of jets at relatively low transverse momentum. In this thesis, the most differential measurement of jet energy loss ever made by the ALICE experiment, as well as the first measurement of jets in heavy-ion collisions using machine learning techniques, will be described. The cone-size dependence of jet quenching is investigated, revealing hints that wider jets lose more energy; an observation consistent with numerous jet quenching models.
