2024/12/19 by Stewart, David, Putschke, Joern · 3 citations
#Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Nuclear Experiment (nucl-ex) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2412.15440
Jets clustered from heavy ion collision measurements combine a dense background of particles with those actually resulting from a hard partonic scattering. The background contribution to jet transverse momentum (pT) may be corrected by subtracting the collision average background; however, the background inhomogeneity limits the resolution of this correction. Many recent studies have embedded jets into heavy ion backgrounds and demonstrated a markedly improved background correction is achievable by using neural networks (NNs) trained with aspects of jet substructure which are used to map measured jet pT to the embedded truth jet pT. However, jet quenching in heavy ion collisions modifies jet substructure, and correspondingly biases the NNs' background corrections. This study investigates those biases by using simulations of jet quenching in central Au+Au collisions at √(sNN)=200 GeV/c with hydrodynamically modeled quark-gluon plasma (QGP) evolution. To demonstrate the magnitude of the effect of such biases in measurement, a leading jet nuclear modification factor (RAA) is calculated and reported using the NN background correction on jets quenched utilizing a brick of QGP.