Unfolding jet substructure observables with a machine learning method at √s = 200 GeV in pp collisions in STAR

Jets are collimated sprays of final-state particles produced from initial hard partonic (quark/gluon) scatterings in particle collisions. Since jets are multi-scale objects that connect asymptotically free partons to confined hadrons, jet substructure measurements in vacuum can provide insight into the parton evolution and the ensuing non-perturbative processes. Jet substructure observables are usually unfolded to correct for detector effects with a binned, one- or two-dimensional Bayesian method. Potentially, it is more desirable to unfold in higher dimensions which can account for the possible correlation in the multi-dimensional observable phase space while simultaneously correcting it.

The STAR experiment recorded data of √s = 200 GeV pp collisions during the 2012 RHIC run. From this dataset, we reconstruct jets with charged particle tracks measured in the Time Projection Chamber and neutral particles measured in the Barrel Electromagnetic Calorimeter. We will present preliminary studies of jet substructure observables unfolded with MultiFold, a machine learning method that simultaneously corrects for multiple observables in an un-binned fashion. We will also preview upcoming correlation measurements across jet substructure observables from the STAR experiment.