DOI: https://doi.org/10.26089/NumMet.2024s03

Feynman integral reduction: balanced reconstruction of sparse rational functions and implementation on supercomputers in a co-design approach

Authors

  • Alexander V. Smirnov
  • Mao Zeng

Keywords:

Feynman integrals
computer algebra
calculation optimization
supercomputer codesign

Abstract

Integration-by-parts (IBP) reduction is one of the essential steps in evaluating Feynman integrals. A modern approach to IBP reduction uses modular arithmetic evaluations at the specific numerical values of parameters with subsequent reconstruction of the analytic rational coefficients. Due to the large number of sample points needed, problems at the frontier of science require an application of supercomputers. In this article, we present a rational function reconstruction method that fully takes advantage of sparsity, combining the balanced reconstruction method and the Zippel method. Additionally, to improve the efficiency of the finite-field IBP reduction runs, at each run several numerical probes are computed simultaneously, which allows to decrease the resource overhead. We describe what performance issues one encounters on the way to an efficient implementation on supercomputers, and how one should co-design the algorithm and the supercomputer infrastructure. We present characteristic examples of IBP-reduction in the case of massless two-loop fourand five-point Feynman diagrams using a development version of FIRE and give illustrative examples mimicking the reduction of coefficients appearing in scattering amplitudes for post-Minkowskian gravitational binary dynamics.


Downloads

Published

2024-12-16

Issue

Section

Methods and algorithms of computational mathematics and their applications

Author Biographies

Alexander V. Smirnov

Mao Zeng

University of Edinburgh,
Higgs Centre for Theoretical Physics
James Clark Maxwell Building, Peter Guthrie Tait Road, EH9 3FD, Edinburgh, United Kingdom
• Royal Society University Research Fellow


References

  1. Vl. V. Voevodin, A. S. Antonov, D. A. Nikitenko, et al., “Supercomputer Lomonosov-2: Large Scale, Deep Monitoring and Fine Analytics for the User Community,” Supercomput. Front. Innov. 6 (2), 4-11 (2019).
    doi 10.14529/jsfi190201
  2. K. G. Chetyrkin and F. V. Tkachov, “Integration by Parts: The Algorithm to Calculate β Functions in 4 Loops,” Nucl. Phys. B 192 (1), 159-204 (1981).
    doi 10.1016/0550-3213(81)90199-1
  3. S. Laporta, “High-Precision Calculation of Multiloop Feynman Integrals by Difference Equations,” Int. J. Mod. Phys. A 15 (32), 5087-5159 (2000).
    doi 10.1142/S0217751X00002159
  4. A. V. Smirnov and A. V. Petukhov, “The Number of Master Integrals is Finite,” Lett. Math. Phys. 97 (1), 37-44 (2011).
    doi 10.1007/s11005-010-0450-0
  5. C. Anastasiou and A. Lazopoulos, “Automatic Integral Reduction for Higher Order Perturbative Calculations,” J. High Energy Phys. No. 7, Article Number 046 (2004).
    doi 10.1088/1126-6708/2004/07/046
  6. A. V. Smirnov and F. S. Chukharev, “FIRE6: Feynman Integral REduction with Modular Arithmetic,” Comput. Phys. Commun. 247, Article Number 106877 (2020).
    doi 10.1016/j.cpc.2019.106877
  7. C. Studerus, “Reduze -- Feynman Integral Reduction in C++,” Comput. Phys. Commun. 181 (7), 1293-1300 (2010).
    doi 10.1016/j.cpc.2010.03.012
  8. P. Maierhöfer, J. Usovitsch, and P. Uwer, “Kira -- A Feynman Integral Reduction Program,” Comput. Phys. Commun. 230, 99-112 (2018).
    doi 10.1016/j.cpc.2018.04.012
  9. J. Klappert, F. Lange, P. Maierhöfer, and J. Usovitsch, “Integral Reduction with Kira 2.0 and Finite Field Methods,” Comput. Phys. Commun. 266, Article Number 108024 (2021).
    doi 10.1016/j.cpc.2021.108024
  10. R. N. Lee, “LiteRed 1.4: A Powerful Tool for Reduction of Multiloop Integrals,” J. Phys. Conf. Ser. 523, Article Number 012059 (2014).
    doi 10.1088/1742-6596/523/1/012059
  11. Z. Wu, J. Boehm, R. Ma, et al., “NeatIBP 1.0, a Package Generating Small-Size Integration-by-Parts Relations for Feynman Integrals,” Comput. Phys. Commun. 295, Article Number 108999 (2024).
    doi 10.1016/j.cpc.2023.108999
  12. X. Guan, X. Liu, Y.-Q. Ma, and W.-H. Wu, “Blade: A Package for Block-Triangular Form Improved Feynman Integrals Decomposition,” arXiv: 2405.14621 [hep-ph].
    doi 10.48550/arXiv.2405.14621
  13. A. V. Belitsky, A. V. Smirnov, and R. V. Yakovlev, “Balancing Act: Multivariate Rational Reconstruction for IBP,” Nucl. Phys. B 993, Article Number 116253 (2023).
    doi 10.1016/j.nuclphysb.2023.116253
  14. M. Monagan, “Maximal Quotient Rational Reconstruction: An Almost Optimal Algorithm for Rational Reconstruction,” Proc. Int. Symp. on Symbolic and Algebraic Computation (2004). 243-249.
    doi 10.1145/1005285.1005321
  15. M. Ben-Or and P. Tiwari, “A Deterministic Algorithm for Sparse Multivariate Polynomial Interpolation,” Proc. 20 Ann. ACM Symp. on Theory of Computing (1988). 301-309.
    doi 10.1145/62212.62241
  16. R. Zippel, “Interpolating Polynomials from Their Values,” J. Symbolic Comput. 9 (3), 375-403 (1990).
    doi 10.1016/S0747-7171(08)80018-1
  17. D. Grigoriev, M. Karpinski, and M. F. Singer, “Computational Complexity of Sparse Rational Interpolation,” SIAM J. Comput. 23 (1), 1-11 (1994).
    doi 10.1137/S0097539791194069
  18. E. Kaltofen, “Greatest Common Divisors of Polynomials Given by Straight-Line Programs,” J. ACM 35 (1), 231-264 (1988).
    doi 10.1145/42267.45069
  19. E. Kaltofen and B. M. Trager, “Computing with Polynomials Given by Black Boxes for Their Evaluations: Greatest Common Divisors, Factorization, Separation of Numerators and Denominators,” J. Symbolic Comput. 9 (3), 301-320 (1990).
    doi 10.1016/S0747-7171(08)80015-6
  20. E. Kaltofen and Z. Yang, “On Exact and Approximate Interpolation of Sparse Rational Functions,” Proc. Int. Symp. on Symbolic Algebraic Computation (2007). 203-210.
    doi 10.1145/1277548.1277577
  21. J. de Kleine, M. Monagan, and A. Wittkopf, “Algorithms for the Non-monic Case of the Sparse Modular GCD Algorithm,” Proc. Int. Symp. on Symbolic Algebraic Computation (2005). 124-131.
    doi 10.1145/1073884.1073903
  22. A. Diaz and E. Kaltofen, “FOXBOX: A System for Manipulating Symbolic Objects in Black Box Representation,” Proc. Int. Symp. on Symbolic Algebraic Computation (1998). 30-37.
    doi 10.1145/281508.281538
  23. E. Kaltofen, W.-s. Lee, and A. A. Lobo, “Early Termination in Ben-Or/Tiwari Sparse Interpolation and a Hybrid of Zippel’s Algorithm,” Proc. Int. Symp. on Symbolic Algebraic Computation (2000). 192-201.
    doi 10.1145/345542.345629
  24. Q.-L. Huang and X.-S. Gao, “Sparse Polynomial Interpolation with Finitely Many Values for the Coefficients,” in Lecture Notes in Computer Science (Springer, Cham, 2017), Vol. 10490, pp. 196-209.
    doi 10.1007/978-3-319-66320-3_15
  25. T. Peraro, “Scattering Amplitudes over Finite Fields and Multivariate Functional Reconstruction,” J. High Energy Phys. No. 12, Article Number 030 (2016).
    doi 10.1007/JHEP12(2016)030
  26. T. Peraro, “FiniteFlow: Multivariate Functional Reconstruction Using Finite Fields and Dataflow Graphs,” J. High Energy Phys. No. 7, Article Number 031 (2019).
    doi 10.1007/JHEP07(2019)031
  27. J. Klappert and F. Lange, “Reconstructing Rational Functions with FireFly,” Comput. Phys. Commun. 247, Article Number 106951 (2020).
    doi 10.1016/j.cpc.2019.106951
  28. J. Klappert, S. Y. Klein, and F. Lange, “Interpolation of Dense and Sparse Rational Functions and Other Improvements in FireFly,” Comput. Phys. Commun. 264, Article Number 107968 (2021).
    doi 10.1016/j.cpc.2021.107968
  29. M. S. Floater and K. Hormann, “Barycentric Rational Interpolation with no Poles and High Rates of Approximation,” Numer. Math. 107 (2), 315-331 (2007).
    doi 10.1007/s00211-007-0093-y
  30. A. Maier, “Scaling up to Multivariate Rational Function Reconstruction,” arXiv: 2409.08757v1 [hep-ph].
    doi 10.48550/arXiv.2409.08757
  31. P. S. Wang, “A p-Adic Algorithm for Univariate Partial Fractions,” Proc. Fourth ACM Symposium on Symbolic and Algebraic Computation (1981). 212-217.
    doi 10.1145/800206.806398
  32. S. Abreu, J. Dormans, F. Febres Cordero, et al., “Analytic Form of Planar Two-Loop Five-Gluon Scattering Amplitudes in QCD,” Phys. Rev. Lett. 122 (8), Article Number 082002 (2019).
    doi 10.1103/PhysRevLett.122.082002
  33. G. De Laurentis and B. Page, “Ans854tze for Scattering Amplitudes from p-Adic Numbers and Algebraic Geometry,” J. High Energy Phys. 2022, Article Number 140 (2022).
    doi 10.1007/JHEP12(2022)140
  34. H. A. Chawdhry, “p-Adic Reconstruction of Rational Functions in Multiloop Amplitudes,” Phys. Rev. D 110 (5), Article Number 056028 (2024).
    doi 10.1103/PhysRevD.110.056028
  35. X. Liu, “Reconstruction of Rational Functions Made Simple,” Phys. Lett. B 850, Article Number 138491 (2024).
    doi 10.1016/j.physletb.2024.138491
  36. A. V. Smirnov and V. A. Smirnov, “How to Choose Master Integrals,” Nucl. Phys. B 960, Article Number 115213 (2020).
    doi 10.1016/j.nuclphysb.2020.115213
  37. J. Usovitsch, “Factorization of Denominators in Integration-by-Parts Reductions,” arXiv: 2002.08173v2 [hep-ph].
    doi 10.48550/arXiv.2002.08173
  38. Z. Bern, E. Herrmann, R. Roiban, et al., “Amplitudes, Supersymmetric Black Hole Scattering at mathcalO(G^5), and Loop Integration,” arXiv: 2406.01554v1[hep-th].
    doi 10.48550/arXiv.2406.01554
  39. K. S. Mokrov, A. V. Smirnov, and M. Zeng, “Rational Function Simplification for Integration-by-Parts Reduction and Beyond,” Numerical Methods and Programming 24 (4), 352-367 (2023).
    doi 10.26089/NumMet.v24r425
  40. FLINT: Fast Library for Number Theory. 2023. Version 3.0.0.
    https://flintlib.org.
  41. B. Ruijl, Symbolica: Modern Computer Algebra.
    https://symbolica.io.
  42. G. M. Kurtzer, V. Sochat, and M. W. Bauer, “Singularity: Scientific Containers for Mobility of Compute,” PLoS ONE 12 (5), Article Number e0177459 (2017).
    doi 10.1371/journal.pone.0177459