English

High-Order Parametrization of the Hypergeometric-Meijer Approximants

High Energy Physics - Theory 2023-08-09 v1

Abstract

In previous articles, we showed that, based on large-order asymptotic behavior, one can approximate a divergent series via the parametrization of a specific hypergeometric approximant. The analytical continuation is then carried out through a Mellin-Barnes integral representation of the hypergeometric approximant or equivalently using an equivalent form of the Meijer G-Function. The parametrization process involves the solution of a non-linear set of coupled equations which is hard to achieve (might be impossible) for high orders using normal PCs. In this work, we extend the approximation algorithm to accommodate any order (high or low) of the given series in a short time. The extension also allows us to employ non-perturbative information like strong-coupling and large-order asymptotic data which are always used to accelerate the convergence. We applied the algorithm for different orders (up to O(2929)) of the ground state energy of the x4x^4 anharmonic oscillator with and without the non-perturbative information. We also considered the available 2020 orders for the ground sate energy of the PT\mathcal{PT}-symmetric ix3ix^{3} anharmonic oscillator as well as the given 2020 orders of its strong-coupling expansion or equivalently the Yang-Lee model. For high order weak-coupling parametrization, accurate results have been obtained for the ground state energy and the non-perturbative parameters describing strong-coupling and large-order asymptotic behaviors. The employment of the non-perturbative data accelerated the convergence very clearly. The High temperature expansion for the susceptibility within the SQSQ lattice has been also considered and led to accurate prediction for the critical exponent and critical temperature.

Keywords

Cite

@article{arxiv.2210.04575,
  title  = {High-Order Parametrization of the Hypergeometric-Meijer Approximants},
  author = {Abouzeid M. Shalaby},
  journal= {arXiv preprint arXiv:2210.04575},
  year   = {2023}
}

Comments

This is a self-contained version of the Hypergeometric-Meijer approximation algorithm we introduced before. In this version, one can parameterize high orders in short time and can accommodate non-perturbative parameters as well