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Regularization of Feynman 4-Loop Integrals with Numerical Integration and Extrapolation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13378))

Abstract

In this paper we continue our recent work on evaluating numerical approximations for a set of 4-loop self-energy integrals required in the computation of higher orders in perturbation theory. The results are given by a Laurent expansion in the dimensional regularization parameter, \(\varepsilon ,\) where \(\varepsilon \) is related to the space-time dimension as \(\nu = 4-2\varepsilon .\) Although the leading-order coefficients for the diagrams with massless internal lines are given in analytic form in the literature, we obtain them using a numerical approach and with modern computational techniques. In a similar manner, we derive results for diagrams with massive lines. The SIMD (Single Instruction, Multiple Data) nature of the computation of loop integrals based on composite lattice rules lends itself to an efficient GPU implementation. We further apply double exponential numerical integration layered over the message passing interface (MPI) parallel platform. Limits of integral sequences (as the regularization parameter tends to zero) are implemented numerically using linear and nonlinear extrapolation procedures. Numerical results are given to illustrate the versatility of the methods and show some robustness with regard to the selection of sequence parameters.

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References

  1. Almulihi, A., de Doncker, E.: Accelerating high-dimensional integration using lattice rules on GPUs. In: Proceedings of the 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. CPS IEEE (2017)

    Google Scholar 

  2. Baikov, B.A., Chetyrkin, K.G.: Four loop massless propagators: an algebraic evaluation of all master integrals. Nucl. Phys. B 837, 186–220 (2010)

    Article  MathSciNet  Google Scholar 

  3. Borowka, S., Heinrich, G., Jahn, S., Jones, S.P., Kerner, M., Schlenk, J.: A GPU compatible quasi-Monte Carlo integrator interfaced to pySecDec. Comput. Phys. Commun. 240, 120–137 (2019). Preprint: arXiv:1811.11720v1 [hep-ph]. https://arxiv.org/abs/1811.11720. https://doi.org/10.1016/j.cpc.2019.02.015

  4. Brezinski, C.: A general extrapolation algorithm. Numer. Math. 35, 175–187 (1980)

    Article  MathSciNet  Google Scholar 

  5. de Doncker, E., Shimizu, Y., Fujimoto, J., Yuasa, F.: Computation of loop integrals using extrapolation. Comput. Phys. Commun. 159, 145–156 (2004)

    Article  Google Scholar 

  6. de Doncker, E., Yuasa, F.: Self-energy Feynman diagrams with four loops and 11 internal lines. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12953, pp. 160–175. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86976-2_11

    Chapter  Google Scholar 

  7. de Doncker, E., Yuasa, F., Almulihi, A., Nakasato, N., Daisaka, H., Ishikawa, T.: Numerical multi-loop integration on heterogeneous many-core processors. J. Phys. Conf. Ser. (JPCS) 1525(012002) (2019). https://doi.org/10.1088/1742-6596/1525/1/012002

  8. de Doncker, E., Yuasa, F., Kato, K., Ishikawa, T., Kapenga, J., Olagbemi, O.: Regularization with numerical extrapolation for finite and UV-divergent multi-loop integrals. Comput. Phys. Commun. 224, 164–185 (2018). https://doi.org/10.1016/j.cpc.2017.11.001

    Article  MathSciNet  Google Scholar 

  9. de Doncker, E., Yuasa, F., Olagbemi, O., Ishikawa, T.: Large scale automatic computations for Feynman diagrams with up to five loops. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12253, pp. 145–162. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58814-4_11

    Chapter  Google Scholar 

  10. L’ Equyer, P., Munger, D.: Algorithm 958: lattice builder: a general software tool for constructing rank-1 lattice rules. ACM Trans. Math. Softw. 42(2), 15:1–15:30 (2016)

    Google Scholar 

  11. Lyman, J.: Optimizing CUDA for GPU architecture – choosing the right dimensions. http://selkie.macalester.edu/csinparallel/modules/CUDAArchitecture/build/html/index.html

  12. Lyness, J.N.: Applications of extrapolation techniques to multidimensional quadrature of some integrand functions with a singularity. J. Comput. Phys. 20, 346–364 (1976)

    Article  MathSciNet  Google Scholar 

  13. Nuyens, D., Cools, R.: Fast algorithms for component-by-component construction of rank-1 lattice rules in shift-invariant reproducing kernel Hilbert spaces. Math. Comp. 75, 903–920 (2006)

    Article  MathSciNet  Google Scholar 

  14. Nuyens, D., Cools, R.: Fast component-by-component construction of rank-1 lattice rules with a non-prime number of points. J. Complex. 22, 4–28 (2006)

    Article  MathSciNet  Google Scholar 

  15. Piessens, R., de Doncker, E., Überhuber, C.W., Kahaner, D.K.: QUADPACK, A Subroutine Package for Automatic Integration, Springer Series in Computational Mathematics, vol. 1. Springer-Verlag, Heidelberg (1983). https://doi.org/10.1007/978-3-642-61786-7

  16. Pittau, R., Webber, B.: Direct numerical evaluation of multi-loop integrals without contour deformation. Eur. Phys. J. C 82(1), 1–22 (2022). https://doi.org/10.1140/epjc/s10052-022-10008-6

    Article  Google Scholar 

  17. Ruijl, B., Ueda, T., Vermaseren, J.A.M.: Forcer, a Form program for the parametric reduction of four-loop massless propagator diagrams. Comput. Phys. Commun. 253, 107198 (2020)

    Article  MathSciNet  Google Scholar 

  18. Shanks, D.: Non-linear transformations of divergent and slowly convergent sequences. J. Math. Phys. 34, 1–42 (1955)

    Article  MathSciNet  Google Scholar 

  19. Sidi, A.: Practical Extrapolation Methods - Theory and Applications. Cambridge University Press (2003). ISBN 0-521-66159-5

    Google Scholar 

  20. Sidi, A.: Extension of a class of periodizing transformations for numerical integration. Math. Comp. 75(253), 327–343 (2005)

    Article  MathSciNet  Google Scholar 

  21. Sloan, I., Joe, S.: Lattice Methods for Multiple Integration. Oxford University Press (1994)

    Google Scholar 

  22. Sugihara, M.: Optimality of the double exponential formula - functional analysis approach. Numer. Math. 75(3), 379–395 (1997)

    Article  MathSciNet  Google Scholar 

  23. Takahasi, H., Mori, M.: Double exponential formulas for numerical integration. Publ. Res. Inst. Math. Sci. 9(3), 721–741 (1974)

    Article  MathSciNet  Google Scholar 

  24. Techpowerup. https://www.techpowerup.com/gpu-specs/quadro-gv100.c3066

  25. Wynn, P.: On a device for computing the \(e_m(s_n)\) transformation. Math. Tables Aids Comput. 10, 91–96 (1956)

    Article  MathSciNet  Google Scholar 

  26. Yuasa, F., et al.: Numerical computation of two-loop box diagrams with masses. J. Comput. Phys. Commun. 183, 2136–2144 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

We acknowledge the support of the Grant-in-Aid for Scientific Research (JP20K03941) from JSPS KAKENHI, and the National Science Foundation Award Number 1126438 that funded work on multivariate integration. The authors of this paper sincerely appreciate the reviewers’ comments, which have helped us improve the paper considerably.

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Appendices

Appendix

A C and D Functions

In the code of the C and D functions below for the diagrams of Fig. 1, we denote \(x_{i_1\ldots i_k} = \sum _{j = i_1}^{i_k} x_j.\)

1.1 A.1 \(M_{43}\) Massive Diagram

figure a

1.2 A.2 \(M_{44}\)

For \(M_{44}\) (and \(M_{45}\)) we use the expression for W from [8], and further

$$\begin{aligned}&U = C \\&V = M^2 - \frac{W}{U} \\&D = UV = U M^2 - W \\&M^2 = \sum _{r=1}^N m_r^2\, x_r \end{aligned}$$

Thus \(M^2 = 1\) for massive diagrams with all masses \(m_r = 1,\) and \(D = U - W = C - W.\)

For massless diagrams, \(M^2 = 0,\) so \(D = -W.\)

figure b

1.3 A.3 \(M_{45}\)

figure c

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de Doncker, E., Yuasa, F. (2022). Regularization of Feynman 4-Loop Integrals with Numerical Integration and Extrapolation. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378. Springer, Cham. https://doi.org/10.1007/978-3-031-10562-3_28

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  • DOI: https://doi.org/10.1007/978-3-031-10562-3_28

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