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• W4387521063 abstract "We present a major update of the program pySecDec, a toolbox for the evaluation of dimensionally regulated parameter integrals. The new version enables the evaluation of multi-loop integrals as well as amplitudes in a highly distributed and flexible way, optionally on GPUs. The program has been optimised and runs up to an order of magnitude faster than the previous release. A new integration procedure that utilises construction-free median Quasi-Monte Carlo rules is implemented. The median lattice rules can outperform our previous component-by-component rules by a factor of 5 and remove the limitation on the maximum number of sampling points. The expansion by regions procedures have been extended to support Feynman integrals with numerators, and functions for automatically determining when and how analytic regulators should be introduced are now available. The new features and performance are illustrated with several examples. Program Title:: pySecDec CPC Library link to program files: https://doi.org/10.17632/dnrkf5jxzh.4 Developer's repository link: https://github.com/gudrunhe/secdec, https://secdec.readthedocs.io (Online documentation) Licensing provisions: GNU Public License v3 Programming language: Python, Form, C++, Cuda External routines/libraries: GSL [1], NumPy [2], SymPy [3], Nauty [4], Cuba [5], Form [6], GiNaC and CLN [7], Normaliz [8], GMP [9]. Journal reference of previous version: Comput. Phys. Commun. 273 (2022) 108267 [1]. Does the new version supersede the previous version?: Yes. Nature of problem: Scattering amplitudes at higher orders in perturbation theory are typically represented as a linear combination of coefficients — containing the kinematic invariants and the space-time dimension — multiplied with loop integrals which contain singularities and whose analytic representation might be unknown. Solution method: Extraction of singularities in the dimensional regularization parameter as well as in analytic regulators for potential spurious singularities is done using sector decomposition. The combined evaluation of the integrals with their coefficients is performed in an efficient way. Restrictions: Depending on the complexity of the problem, limited by memory and CPU/GPU time. M. Galassi et al, GNU Scientific Library Reference Manual. ISBN:0954612078, http://www.gnu.org/software/gsl/. C. R. Harris, K. J. Millman, S. J. van der Walt, et al, Array programming with NumPy, Nature 585 (2020) 357–362. 10.1038/s41586-020-2649-2, http://www.numpy.org/. A. Meurer, et al., SymPy: symbolic computing in Python, PeerJ Comp. Sci. 3 (2017) e103. 10.7717/peerj-cs.103, http://www.sympy.org/. B. D. McKay and A. Piperno, Practical graph isomorphism, II, J. Symb. Comput. 60 (2014) 94–112. 10.1016/j.jsc.2013.09.003, http://pallini.di.uniroma1.it. T. Hahn, CUBA: A Library for multidimensional numerical integration, Comput. Phys. Commun. 168 (2005) 78. arXiv: hep-ph/0404043, http://www.feynarts.de/cuba/. J. Kuipers, T. Ueda and J. A. M. Vermaseren, Code Optimization in FORM, Comput. Phys. Commun. 189 (2015) 1. arXiv: 1310.7007, http://www.nikhef.nl/~form/. C. W. Bauer, A. Frink, and R. B. Kreckel, Introduction to the GiNaC framework for symbolic computation within the C++ programming language, J. Symb. Comput. 33 (2002) 1–12. arXiv:cs/0004015, https://www.ginac.de/. W. Bruns, B. Ichim, B. and T. Römer, C. Söger, Normaliz. Algorithms for rational cones and affine monoids. http://www.math.uos.de/normaliz/. T. Granlund et al, GMP: The GNU Multiple Precision Arithmetic Library. https://gmplib.org/." @default.
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• W4387521063 date "2023-10-01" @default.
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• W4387521063 title "Numerical scattering amplitudes with pySecDec" @default.
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