High Energy / Nuclear Theory / RIKEN Seminars

Nuclear structure from ultrarelativistic collisions; Methods and preliminary results

by Matthew Luzum (Universidade de São Paulo)

US/Eastern
CFNS Library ( https://bnl.zoomgov.com/j/1614715193?pwd=WkwxODVWdzZzb29zQnZRVGp3VTBDQT09)

CFNS Library

https://bnl.zoomgov.com/j/1614715193?pwd=WkwxODVWdzZzb29zQnZRVGp3VTBDQT09

Description

It has become increasingly clear that the structure of atomic nuclei can be probed with ultrarelativistic collisions, despite the enormous difference in energy scales between typical nuclear processes and high-energy collisions as performed at colliders such as RHIC and the LHC.  In particular, high-precision data from collisions performed with pairs of isobaric species can allow for accurate relative measurements of nuclear properties.  A systematic study requires a variation of parameters representing nuclear properties such as radius, skin thickness, angular deformation, and short-range correlations, to determine the sensitivity of the various observables on each of these properties.   Until now such studies have been
limited due to the computational cost associated with sufficient suppression of statistical uncertainty.   In this talk I propose a method for efficiently carrying out such study, based on the shifting of positions of nucleons in Monte-Carlo samples. We show that by using this method, statistical demands can be dramatically reduced ---
potentially reducing the required number of simulated events by orders of magnitude --- paving the way for systematic study of nuclear structure in high-energy collisions.

As an application, I show the results of a preliminary initial-state study, including a sensitivity analysis and a Bayesian closure test using observables and uncertainties as measured in the RHIC isobar run.  With this we can predict the precision with which we can extract
nuclear properties from existing measurements, and which new observables could be measured to probe other properties.

Organised by

Yacine Mehtar-Tani