Codebase release r1.4 for CoVVVR

Journal Article · 03 March 2024 · SciPost Physics Codebases

DOI: https://doi.org/10.21468/SciPostPhysCodeb.28-r1.4 · OSTI ID:2318571

Shyamsundar, Prasanth; Scott, Jacob L.; Mrenna, Stephen; Matchev, Konstantin T.; Kong, Kyoungchul

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named COVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-07CH11359; SC0019219; SC0022148; SC0024407; SC0024673
• OSTI ID: 2318571
• Journal Information: SciPost Physics Codebases, Journal Name: SciPost Physics Codebases; ISSN 2949-804X
• Publisher: Stichting SciPostCopyright Statement
• Country of Publication: Netherlands
• Language: English