%0 Generic %1 orjuelaquintana2022using %A Orjuela-Quintana, J. Bayron %A Nesseris, Savvas %A Cardona, Wilmar %D 2022 %K cosmology lss machine_learning phd %T Using machine learning to compress the matter transfer function $T(k)$ %U http://arxiv.org/abs/2211.06393 %X The linear matter power spectrum $P(k,z)$ connects theory with large scale structure observations in cosmology. Its scale dependence is entirely encoded in the matter transfer function $T(k)$, which can be computed numerically by Boltzmann solvers, and can also be computed semi-analytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulae. However, both the BBKS and EH formulae have some significant drawbacks. On the one hand, although BBKS is a simple expression, it is only accurate up to $10\%$, which is well above the $1\%$ precision goal of forthcoming surveys. On the other hand, while EH is as accurate as required by upcoming experiments, it is a rather long and complicated expression. Here, we use the Genetic Algorithms (GAs), a particular machine learning technique, to derive simple and accurate fitting formulae for the transfer function $T(k)$. When the effects of massive neutrinos are also considered, our expression slightly improves over the EH formula, while being notably shorter in comparison.

@misc{orjuelaquintana2022using, abstract = {The linear matter power spectrum $P(k,z)$ connects theory with large scale structure observations in cosmology. Its scale dependence is entirely encoded in the matter transfer function $T(k)$, which can be computed numerically by Boltzmann solvers, and can also be computed semi-analytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulae. However, both the BBKS and EH formulae have some significant drawbacks. On the one hand, although BBKS is a simple expression, it is only accurate up to $10\%$, which is well above the $1\%$ precision goal of forthcoming surveys. On the other hand, while EH is as accurate as required by upcoming experiments, it is a rather long and complicated expression. Here, we use the Genetic Algorithms (GAs), a particular machine learning technique, to derive simple and accurate fitting formulae for the transfer function $T(k)$. When the effects of massive neutrinos are also considered, our expression slightly improves over the EH formula, while being notably shorter in comparison.}, added-at = {2023-01-01T13:51:04.000+0100}, author = {Orjuela-Quintana, J. Bayron and Nesseris, Savvas and Cardona, Wilmar}, biburl = {https://www.bibsonomy.org/bibtex/264722e6b02f679bc73483feecf40d84b/intfxdx}, description = {Using machine learning to compress the matter transfer function $T(k)$}, interhash = {d768d474e3bf4293ec18d1435faa7a61}, intrahash = {64722e6b02f679bc73483feecf40d84b}, keywords = {cosmology lss machine_learning phd}, note = {cite arxiv:2211.06393Comment: 11 pages, 5 figures, 2 tables}, timestamp = {2023-01-01T13:51:04.000+0100}, title = {Using machine learning to compress the matter transfer function $T(k)$}, url = {http://arxiv.org/abs/2211.06393}, year = 2022 }