Forcer, a FORM program for the parametric reduction of four-loop massless propagator diagrams
B. Ruijl (NIKHEF, Amsterdam & Leiden U.), T. Ueda, J.A.M. Vermaseren, (NIKHEF, Amsterdam)

TL;DR
Forcer is a FORM-based program designed to automate the reduction of four-loop massless propagator integrals to master integrals, enabling efficient parametric IBP reductions similar to existing three-loop tools.
Contribution
The paper introduces Forcer, a novel automated tool for four-loop integral reduction, including its structure, automation, and application examples, advancing computational techniques in high-loop calculations.
Findings
Successfully reduces four-loop integrals to master integrals
Demonstrates performance and efficiency of the Forcer program
Provides a framework for automating complex IBP reductions
Abstract
We explain the construction of Forcer, a FORM program for the reduction of four-loop massless propagator-type integrals to master integrals. The resulting program performs parametric IBP reductions similar to the three-loop Mincer program. We show how one can solve many systems of IBP identities parametrically in a computer-assisted manner. Next, we discuss the structure of the Forcer program, which involves recognizing reduction actions for each topology, applying symmetries, and transitioning between topologies after edges have been removed. This part is entirely precomputed and automatically generated. We give examples of recent applications of Forcer, and study the performance of the program. Finally we demonstrate how to use the Forcer package and sketch how to prepare physical diagrams for evaluation by Forcer.
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
TopicsProtein Structure and Dynamics · Numerical methods for differential equations · Model Reduction and Neural Networks
