Reweighting the quark Sivers function with STAR jet data

TL;DR

This paper demonstrates the first application of Bayesian reweighting to the quark Sivers function, integrating new STAR jet data to enhance understanding of its x-dependence without re-fitting, and extending the method to asymmetric errors.

Contribution

It introduces a novel Bayesian reweighting approach for TMD distributions, specifically applied to the quark Sivers function, incorporating new experimental data efficiently.

Findings

Significant improvement in the knowledge of the quark Sivers function.

Extended reweighting method to handle asymmetric errors.

Broader x-region explored compared to previous SIDIS data.

Abstract

The Bayesian reweighting procedure is applied for the first time to a TMD distribution, the quark Sivers function extracted from SIDIS data. By exploiting the recent published single spin asymmetry data for the inclusive jet production in $p^\uparrow p$ collisions from the STAR collaboration at RHIC, we show how such a procedure allows to incorporate the information contained in the new data set, without the need of re-fitting, and to explore a much wider $x$ region compared to SIDIS measurements. The reweighting method is also extended to the case of asymmetric errors, and the results show a significant improvement on the knowledge of the quark Sivers function.