Speaker
Description
Heavy flavor (charm and bottom) production is a unique probe to test the perturbative Quantum Chromodynamics (pQCD) and study the transport properties of nuclear media. Heavy flavor signal searching is one of the most challenging measurements in collider experiments due to their ultra-low production rate and extensive backgrounds. The brand new sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) is optimized for precise heavy flavor measurements to reveal the properties of quark gluon plasma. The future Electron-Ion Collider (EIC) will utilize high-luminosity high-energy electron+proton ($e+p$) and electron+nucleus ($e+A$) collisions at different center of mass energies (29 - 141 GeV) to solve several fundamental questions such as the hadronization mechanism. We will present the heavy flavor jet studies using both the traditional selection methods and new Machine Learning (ML) algorithms in sPHENIX 200 GeV $p$+$p$ simulation. A Graph Neural Network (GNN) is used to tag the jet flavor, and is expected to significantly enhance the jet identification performance especially for bottom and charm jets. A series of heavy flavor hadron and jet physics studies have been carried out in standalone simulations with parameterized EIC detector performance. We will present the open heavy hadron and jet reconstruction capabilities of the EIC and the associated physics projections in comparison with recent theoretical calculations. The impacts of these studies to advance our understanding of the flavor dependent parton energy loss and the flavor dependent hadronization process will be discussed as well.