Cutting Multiparticle Correlators Down to Size
Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler

TL;DR
This paper introduces a graph-theoretic approach to multiparticle correlators, enabling systematic classification, faster computation, and new applications in jet substructure analysis at colliders.
Contribution
It develops a novel graph-based framework for analyzing multiparticle correlators, reducing computational complexity and introducing Energy Flow Moments for efficient collider observable calculations.
Findings
Correlators can be computed in linear time when pairwise distances are inner products.
New tensorial objects called Energy Flow Moments improve jet substructure analysis.
Computed the number of leafless multigraphs up to 16 edges, matching the count of independent kinematic polynomials.
Abstract
Multiparticle correlators are mathematical objects frequently encountered in quantum field theory and collider physics. By translating multiparticle correlators into the language of graph theory, we can gain new insights into their structure as well as identify efficient ways to manipulate them. In this paper, we highlight the power of this graph-theoretic approach by "cutting open" the vertices and edges of the graphs, allowing us to systematically classify linear relations among multiparticle correlators and develop faster methods for their computation. The naive computational complexity of an -point correlator among particles is , but when the pairwise distances between particles can be cast as an inner product, we show that all such correlators can be computed in linear runtime. With the help of new tensorial objects called Energy Flow…
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