$\nu$-point energy correletors with FastEEC: small-$x$ physics from LHC jets
Ankita Budhraja, Hao Chen, Wouter J. Waalewijn

TL;DR
This paper introduces a computational breakthrough enabling the calculation of $ u$-point energy correlators for LHC jet data, revealing insights into small-$x$ physics and QCD evolution through a new efficient method called FastEEC.
Contribution
The paper develops a recursive and dynamic subjet resolution method, FastEEC, to efficiently compute $ u$-correlators for LHC data, facilitating novel small-$x$ physics studies.
Findings
$ u$-correlators computed for LHC jets match DGLAP evolution.
At small $ u$, anomalous dimension saturates to BFKL value.
FastEEC enables practical analysis of non-integer energy correlators.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · High-Energy Particle Collisions Research · Computational Physics and Python Applications
