Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
Abstract This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-04-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-023-11435-9 |
| _version_ | 1827943254979510272 |
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| author | Isidro Gómez-Vargas Ricardo Medel-Esquivel Ricardo García-Salcedo J. Alberto Vázquez |
| author_facet | Isidro Gómez-Vargas Ricardo Medel-Esquivel Ricardo García-Salcedo J. Alberto Vázquez |
| author_sort | Isidro Gómez-Vargas |
| collection | DOAJ |
| description | Abstract This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $$f\sigma _8$$ f σ 8 measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation. |
| first_indexed | 2024-03-13T10:12:40Z |
| format | Article |
| id | doaj.art-8703003c1174414c8a08fa7b968d0048 |
| institution | Directory Open Access Journal |
| issn | 1434-6052 |
| language | English |
| last_indexed | 2024-03-13T10:12:40Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj.art-8703003c1174414c8a08fa7b968d00482023-05-21T11:24:57ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522023-04-0183411810.1140/epjc/s10052-023-11435-9Neural network reconstructions for the Hubble parameter, growth rate and distance modulusIsidro Gómez-Vargas0Ricardo Medel-Esquivel1Ricardo García-Salcedo2J. Alberto Vázquez3ICF, Universidad Nacional Autónoma de MéxicoICF, Universidad Nacional Autónoma de MéxicoCICATA-Legaria, Instituto Politécnico NacionalICF, Universidad Nacional Autónoma de MéxicoAbstract This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $$f\sigma _8$$ f σ 8 measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation.https://doi.org/10.1140/epjc/s10052-023-11435-9 |
| spellingShingle | Isidro Gómez-Vargas Ricardo Medel-Esquivel Ricardo García-Salcedo J. Alberto Vázquez Neural network reconstructions for the Hubble parameter, growth rate and distance modulus European Physical Journal C: Particles and Fields |
| title | Neural network reconstructions for the Hubble parameter, growth rate and distance modulus |
| title_full | Neural network reconstructions for the Hubble parameter, growth rate and distance modulus |
| title_fullStr | Neural network reconstructions for the Hubble parameter, growth rate and distance modulus |
| title_full_unstemmed | Neural network reconstructions for the Hubble parameter, growth rate and distance modulus |
| title_short | Neural network reconstructions for the Hubble parameter, growth rate and distance modulus |
| title_sort | neural network reconstructions for the hubble parameter growth rate and distance modulus |
| url | https://doi.org/10.1140/epjc/s10052-023-11435-9 |
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