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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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)
Baikov, B.A., Chetyrkin, K.G.: Four loop massless propagators: an algebraic evaluation of all master integrals. Nucl. Phys. B 837, 186–220 (2010)
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
Brezinski, C.: A general extrapolation algorithm. Numer. Math. 35, 175–187 (1980)
de Doncker, E., Shimizu, Y., Fujimoto, J., Yuasa, F.: Computation of loop integrals using extrapolation. Comput. Phys. Commun. 159, 145–156 (2004)
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
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
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
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
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)
Lyman, J.: Optimizing CUDA for GPU architecture – choosing the right dimensions. http://selkie.macalester.edu/csinparallel/modules/CUDAArchitecture/build/html/index.html
Lyness, J.N.: Applications of extrapolation techniques to multidimensional quadrature of some integrand functions with a singularity. J. Comput. Phys. 20, 346–364 (1976)
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)
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)
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
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
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)
Shanks, D.: Non-linear transformations of divergent and slowly convergent sequences. J. Math. Phys. 34, 1–42 (1955)
Sidi, A.: Practical Extrapolation Methods - Theory and Applications. Cambridge University Press (2003). ISBN 0-521-66159-5
Sidi, A.: Extension of a class of periodizing transformations for numerical integration. Math. Comp. 75(253), 327–343 (2005)
Sloan, I., Joe, S.: Lattice Methods for Multiple Integration. Oxford University Press (1994)
Sugihara, M.: Optimality of the double exponential formula - functional analysis approach. Numer. Math. 75(3), 379–395 (1997)
Takahasi, H., Mori, M.: Double exponential formulas for numerical integration. Publ. Res. Inst. Math. Sci. 9(3), 721–741 (1974)
Techpowerup. https://www.techpowerup.com/gpu-specs/quadro-gv100.c3066
Wynn, P.: On a device for computing the \(e_m(s_n)\) transformation. Math. Tables Aids Comput. 10, 91–96 (1956)
Yuasa, F., et al.: Numerical computation of two-loop box diagrams with masses. J. Comput. Phys. Commun. 183, 2136–2144 (2012)
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
1.2 A.2 \(M_{44}\)
For \(M_{44}\) (and \(M_{45}\)) we use the expression for W from [8], and further
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 \(D = -W.\)
1.3 A.3 \(M_{45}\)
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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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