$\nu$-point energy correletors with FastEEC: small-$x$ physics from LHC jets
Abstract
In recent years, energy correlators have emerged as a powerful tool for studying jet substructure, with promising applications such as probing the hadronization transition, analyzing the quark-gluon plasma, and improving the precision of top quark mass measurements. The projected -point correlator measures correlations between final-state particles by tracking the largest separation between them, showing a scaling behavior related to DGLAP splitting functions. These correlators can be analytically continued in , commonly referred to as -correlators, allowing access to non-integer moments of the splitting functions. Of particular interest is the limit, where the small momentum fraction behavior of the splitting functions requires resummation. Originally, the computational complexity of evaluating -correlators for particles scaled as , making it impractical for real-world analyses. However, by using recursion, we reduce this to , and through the FastEEC method of dynamically resolving subjets, is replaced by the number of subjets. This breakthrough enables, for the first time, the computation of -correlators for LHC data. In practice, limiting the number of subjets to 16 is sufficient to achieve percent-level precision, which we validate using known integer- results and convergence tests for non-integer . We have implemented this in an update to FastEEC and conducted an initial study of power-law scaling in the perturbative regime as a function of , using CMS Open Data on jets. The results agree with DGLAP evolution, except at small , where the anomalous dimension saturates to a value that matches the BFKL anomalous dimension.
Keywords
Cite
@article{arxiv.2409.12235,
title = {$\nu$-point energy correletors with FastEEC: small-$x$ physics from LHC jets},
author = {Ankita Budhraja and Hao Chen and Wouter J. Waalewijn},
journal= {arXiv preprint arXiv:2409.12235},
year = {2025}
}
Comments
16 pages, 6 figures. v2: journal version. Associated code available at: https://github.com/abudhraj/FastEEC/releases/tag/0.2