Using machine learning to compress the matter transfer function T (k)
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1103/PhysRevD.107.083520 http://hdl.handle.net/11449/248801 |
Resumo: | 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 semianalytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulas. However, both the BBKS and EH formulas 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 formulas 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. |
id |
UNSP_d33218a3566bc7d02c5f5e067f45cd04 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/248801 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Using machine learning to compress the matter transfer function T (k)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 semianalytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulas. However, both the BBKS and EH formulas 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 formulas 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.Departamento de Física Universidad Del Valle Ciudad Universitaria MeléndezInstituto de Física Teórica UAM-CSIC Universidad Autonóma de Madrid, CantoblancoICTP South American Institute for Fundamental Research Instituto de Física Teórica Universidade Estadual PaulistaICTP South American Institute for Fundamental Research Instituto de Física Teórica Universidade Estadual PaulistaCiudad Universitaria MeléndezUniversidad Autonóma de MadridUniversidade Estadual Paulista (UNESP)Orjuela-Quintana, J. BayronNesseris, SavvasCardona, Wilmar [UNESP]2023-07-29T13:54:06Z2023-07-29T13:54:06Z2023-04-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1103/PhysRevD.107.083520Physical Review D, v. 107, n. 8, 2023.2470-00292470-0010http://hdl.handle.net/11449/24880110.1103/PhysRevD.107.0835202-s2.0-85158875982Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPhysical Review Dinfo:eu-repo/semantics/openAccess2023-07-29T13:54:06Zoai:repositorio.unesp.br:11449/248801Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:54:06Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using machine learning to compress the matter transfer function T (k) |
title |
Using machine learning to compress the matter transfer function T (k) |
spellingShingle |
Using machine learning to compress the matter transfer function T (k) Orjuela-Quintana, J. Bayron |
title_short |
Using machine learning to compress the matter transfer function T (k) |
title_full |
Using machine learning to compress the matter transfer function T (k) |
title_fullStr |
Using machine learning to compress the matter transfer function T (k) |
title_full_unstemmed |
Using machine learning to compress the matter transfer function T (k) |
title_sort |
Using machine learning to compress the matter transfer function T (k) |
author |
Orjuela-Quintana, J. Bayron |
author_facet |
Orjuela-Quintana, J. Bayron Nesseris, Savvas Cardona, Wilmar [UNESP] |
author_role |
author |
author2 |
Nesseris, Savvas Cardona, Wilmar [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Ciudad Universitaria Meléndez Universidad Autonóma de Madrid Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Orjuela-Quintana, J. Bayron Nesseris, Savvas Cardona, Wilmar [UNESP] |
description |
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 semianalytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulas. However, both the BBKS and EH formulas 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 formulas 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:54:06Z 2023-07-29T13:54:06Z 2023-04-15 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1103/PhysRevD.107.083520 Physical Review D, v. 107, n. 8, 2023. 2470-0029 2470-0010 http://hdl.handle.net/11449/248801 10.1103/PhysRevD.107.083520 2-s2.0-85158875982 |
url |
http://dx.doi.org/10.1103/PhysRevD.107.083520 http://hdl.handle.net/11449/248801 |
identifier_str_mv |
Physical Review D, v. 107, n. 8, 2023. 2470-0029 2470-0010 10.1103/PhysRevD.107.083520 2-s2.0-85158875982 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Physical Review D |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803650161540333568 |