Solving the $H_{0}$ tension in $f(T)$ Gravity through Bayesian Machine Learning
| Autor: | Muhsin Aljaf, Emilio Elizalde, Martiros Khurshudyan, Kairat Myrzakulov, Aliya Zhadyranova |
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| Přispěvatelé: | Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Generalitat de Catalunya, Ministry of Education and Science (Kazakhstan) |
| Rok vydání: | 2022 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2205.06252 |
| Popis: | Bayesian Machine Learning (BML) and strong lensing time delay (SLTD) techniques are used in order to tackle the H0 tension in f(T) gravity. The power of BML relies on employing a model-based generative process which already plays an important role in different domains of cosmology and astrophysics, being the present work a further proof of this. Three viable f(T) models are considered: a power law, an exponential, and a squared exponential model. The learned constraints and respective results indicate that the exponential model, f(T)=αT0(1−e−pT/T0), has the capability to solve the H0 tension quite efficiently. The forecasting power and robustness of the method are shown by considering different redshift ranges and parameters for the lenses and sources involved. The lesson learned is that these values can strongly affect our understanding of the H0 tension, as it does happen in the case of the model considered. The resulting constraints of the learning method are eventually validated by using the observational Hubble data (OHD). This work has been partially supported by MICINN (Spain), project PID2019-104397GB-I00, of the Spanish State Research Agency program AEI/10.13039/501100011033, by the Catalan Government, AGAUR project 2017-SGR-247, and by the program Unidad de Excelencia Maria de Maeztu CEX2020-001058-M. This paper was supported by the Ministry of Education and Science of the Republic of Kazakhstan, grant AP08052034. MK has been supported by the Juan de la Cierva-incorporacion grant (IJC2020-042690-I). |
| Databáze: | OpenAIRE |
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