NPA Virtual Seminar, Raghav Kunnawalkam Elayavalli, Wayne State University, “Era of Jet-SubStructure and its utility in proton-proton and heavy-ion collisions”

This is a virtual seminar.
Abstract: Jets, originating from hard scatterings of quarks/gluons (partons), have been established as an important probe of the Quark-Gluon Plasma (QGP), resulting in the discovery of parton energy loss or jet quenching. A common method of measuring the effects of jet quenching is by comparing properties of jets in proton-proton (pp) collisions as a vacuum/non-QGP baseline to their counterpart in heavy ion (AA) collisions. Any modifications to these observables can then point to the QGP transport properties which are inferred from theoretical/Monte-Carlo calculations. Since jet evolution intimately depends on both the momentum and angular scales, disentangling these scales via jet substructure (JSS) tools can lead to a more differential study of parton energy loss aimed at extracting the fundamental properties such as QGP coherence length and quantify soft gluon emissions via the LPM effect. In essence, understanding parton evolution and JSS in vacuum is a necessary prerequisite for knowing how jets are quenched in the QGP, which is in turn required for extracting its microscopic properties via jet-tomography. In this talk, I will introduce JSS and present recent measurements from the STAR collaboration of JSS observables in p-p and Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV. These measurements are aimed at describing the vacuum parton shower and utilize JSS tools to isolate a special selection of jets in heavy ion collisions who’s energy loss mechanism can then be studied in a differential fashion. I will conclude the talk by contextualizing these measurements and briefly discuss my interests with upcoming heavy ion runs at RHIC and at the recently approved EIC, where JSS can contribute towards studies of quantum entanglement and hadronization within jets in a clean environment.
Zoom link: https://yale.zoom.us/j/81923393236?pwd=dUY0TWxsVFZkV3lUeGZ4VHBwU2JUUT09