Jet Tomography in Heavy-Ion Collisions with Deep Learning - Du, Yi-Lun; Pablos, Daniel; Tywoniuk, Konrad (Physical review letters, 2022)
Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final, energy. We show how this new technique provides unique access to the genuine configuration profile of jets over the transverse plane of the nuclear collision, both with respect to their production point and their orientation. By effectively removing the selection biases induced by final-state interactions, one can analyze the potential azimuthal anisotropies of jet production associated to initial-state effects. Additionally, we demonstrate the capability of our new method to locate with precision the production point of a dijet pair in the nuclear overlap region, in what constitutes an important step forward toward the long term quest of using jets as tomographic probes of the quark-gluon plasma.