NPA Seminar, Daniel Pablos, INFN, “Deep Learning Jet Modifications in Heavy Ion Collisions”

High-energy jets experience severe modifications along their passage through the quark-gluon plasma (QGP), a new state of matter created in ultra-relativistic heavy ion collisions. Due to the steeply falling jet spectrum, when we measure jets we are typically looking at the jets that lost the least energy. This selection bias hinders our ability to study true jet modifications, resulting in a more limited knowledge of the way the QGP interacts with the energetic partons. Using deep learning techniques we can, to a large extent, remove such bias by selecting jets based on the energy they would have had in the absence of the medium, enhancing in this way the relative weight of truly modified jets. Along with the analysis of jet substructure observables, I will show the new avenues with which we can perform jet tomography studies, both by recovering the underlying path-length distributions and initial jet azimuthal anisotropies.
In-person attendance will be capped at 20 people on a first-come, first-served basis, according to the current Yale policies.
More Information: https://covid19.yale.edu/campus-life/events-gatherings-meetings
Please email the host for the Zoom connection information.
Host: Laura Havener
laura.havener@yale.edu