Solving the H tension in f(T) gravity through Bayesian machine learning
Aljaf M. Elizalde E. Khurshudyan M. Myrzakulov K. Zhadyranova A.
December 2022Institute for Ionics
European Physical Journal C
2022#82Issue 12
Bayesian Machine Learning (BML) and strong lensing time delay (SLTD) techniques are used in order to tackle the H 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 H 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 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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Department of Physics, Oakland University, Rochester, 48309, MI, United States
Department of Physics, College of Education, University of Garmian, Kurdistan Region, Iraq
Consejo Superior de Investigaciones Científicas, ICE/CSIC-IEEC, Carrer de Can Magrans s/n, Barcelona, Campus UAB, Bellaterra, 08193, Spain
Eurasian National University, Nur-Sultan, 010008, Kazakhstan
Ratbay Myrzakulov Eurasian International Centre for Theoretical Physics, Nur-Sultan, 010009, Kazakhstan
Department of Physics
Department of Physics
Consejo Superior de Investigaciones Científicas
Eurasian National University
Ratbay Myrzakulov Eurasian International Centre for Theoretical Physics
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