Solving the $H_{0}$ tension in $f(T)$ Gravity through Bayesian Machine Learning. (arXiv:2205.06252v1 [astro-ph.CO])

<a href="http://arxiv.org/find/astro-ph/1/au:+Aljaf_M/0/1/0/all/0/1">Muhsin Aljaf</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Elizalde_E/0/1/0/all/0/1">Emilio Elizalde</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Khurshudyan_M/0/1/0/all/0/1">Martiros Khurshudyan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Myrzakulov_K/0/1/0/all/0/1">Kairat Myrzakulov</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zhadyranova_A/0/1/0/all/0/1">Aliya Zhadyranova</a>

Bayesian Machine Learning(BML) and strong lensing time delay(SLTD) techniques

are used in order to tackle the $H_{0}$ 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,

$f(T)=alpha T_{0}left(1-e^{-pT/T_{0}}right)$, has the capability to solve

the $H_{0}$ 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 $H_{0}$ 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).

Bayesian Machine Learning(BML) and strong lensing time delay(SLTD) techniques

are used in order to tackle the $H_{0}$ 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,

$f(T)=alpha T_{0}left(1-e^{-pT/T_{0}}right)$, has the capability to solve

the $H_{0}$ 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 $H_{0}$ 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).

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