Paper doi abstract bibtex

The purely numerical evaluation of multi-loop integrals and amplitudes can be a viable alternative to analytic approaches, in particular in the presence of several mass scales, provided sufficient accuracy can be achieved in an acceptable amount of time. For many multi-loop integrals, the fraction of time required to perform the numerical integration is significant and it is therefore beneficial to have efficient and well-implemented numerical integration methods. With this goal in mind, we present a new stand-alone integrator based on the use of (quasi-Monte Carlo) rank-1 shifted lattice rules. For integrals with high variance we also implement a variance reduction algorithm based on fitting a smooth function to the inverse cumulative distribution function of the integrand dimension-by-dimension. Additionally, the new integrator is interfaced to pySecDec to allow the straightforward evaluation of multi-loop integrals and dimensionally regulated parameter integrals. In order to make use of recent advances in parallel computing hardware, our integrator can be used both on CPUs and CUDA compatible GPUs where available.

@article{borowka_gpu_2019, title = {A {GPU} compatible quasi-{Monte} {Carlo} integrator interfaced to {pySecDec}}, volume = {240}, issn = {00104655}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0010465519300670}, doi = {10.1016/j.cpc.2019.02.015}, abstract = {The purely numerical evaluation of multi-loop integrals and amplitudes can be a viable alternative to analytic approaches, in particular in the presence of several mass scales, provided sufficient accuracy can be achieved in an acceptable amount of time. For many multi-loop integrals, the fraction of time required to perform the numerical integration is significant and it is therefore beneficial to have efficient and well-implemented numerical integration methods. With this goal in mind, we present a new stand-alone integrator based on the use of (quasi-Monte Carlo) rank-1 shifted lattice rules. For integrals with high variance we also implement a variance reduction algorithm based on fitting a smooth function to the inverse cumulative distribution function of the integrand dimension-by-dimension. Additionally, the new integrator is interfaced to pySecDec to allow the straightforward evaluation of multi-loop integrals and dimensionally regulated parameter integrals. In order to make use of recent advances in parallel computing hardware, our integrator can be used both on CPUs and CUDA compatible GPUs where available.}, language = {en}, urldate = {2020-02-24}, journal = {Computer Physics Communications}, author = {Borowka, S. and Heinrich, G. and Jahn, S. and Jones, S.P. and Kerner, M. and Schlenk, J.}, month = jul, year = {2019}, note = {preprint: http://dro.dur.ac.uk/27775/1/27775.pdf}, keywords = {Feynman diagrams, computational physics, numerical integration, perturbation theory, uses sympy}, pages = {120--137}, }

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