Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression
The current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including p...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2218-1997/10/12/464 |
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| author | José de Jesús Velázquez Luis A. Escamilla Purba Mukherjee J. Alberto Vázquez |
| author_facet | José de Jesús Velázquez Luis A. Escamilla Purba Mukherjee J. Alberto Vázquez |
| author_sort | José de Jesús Velázquez |
| collection | DOAJ |
| description | The current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>CDM and the predicted value of the Hubble parameter at redshift zero is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>H</mi><mn>0</mn></msub><mo>=</mo><mn>68.798</mn><mo>±</mo><mn>6.340</mn><mrow><mo>(</mo><mn>1</mn><mi>σ</mi><mo>)</mo></mrow><mspace width="4.pt"></mspace><mi>km</mi><mspace width="4.pt"></mspace><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mspace width="4.pt"></mspace><msup><mi>Mpc</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>. |
| format | Article |
| id | doaj-art-c4d51a4a942d483b8256919979f824f0 |
| institution | Kabale University |
| issn | 2218-1997 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Universe |
| spelling | doaj-art-c4d51a4a942d483b8256919979f824f02024-12-27T14:57:19ZengMDPI AGUniverse2218-19972024-12-01101246410.3390/universe10120464Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes RegressionJosé de Jesús Velázquez0Luis A. Escamilla1Purba Mukherjee2J. Alberto Vázquez3Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito de la Investigación Científica Ciudad Universitaria, Mexico City 04510, MexicoInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, MexicoCentre for Theoretical Physics, Jamia Millia Islamia, New Delhi 110025, IndiaInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, MexicoThe current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>CDM and the predicted value of the Hubble parameter at redshift zero is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>H</mi><mn>0</mn></msub><mo>=</mo><mn>68.798</mn><mo>±</mo><mn>6.340</mn><mrow><mo>(</mo><mn>1</mn><mi>σ</mi><mo>)</mo></mrow><mspace width="4.pt"></mspace><mi>km</mi><mspace width="4.pt"></mspace><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mspace width="4.pt"></mspace><msup><mi>Mpc</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>.https://www.mdpi.com/2218-1997/10/12/464cosmologydark energyHubble parameterdeceleration parameterlinear regressiongaussian process |
| spellingShingle | José de Jesús Velázquez Luis A. Escamilla Purba Mukherjee J. Alberto Vázquez Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression Universe cosmology dark energy Hubble parameter deceleration parameter linear regression gaussian process |
| title | Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression |
| title_full | Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression |
| title_fullStr | Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression |
| title_full_unstemmed | Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression |
| title_short | Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression |
| title_sort | non parametric reconstruction of cosmological observables using gaussian processes regression |
| topic | cosmology dark energy Hubble parameter deceleration parameter linear regression gaussian process |
| url | https://www.mdpi.com/2218-1997/10/12/464 |
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