Solving the H0 tension in f(T) gravity through Bayesian machine learning

被引：0

作者：

Aljaf, Muhsin ^{[1
,2
]}

Elizalde, Emilio ^{[3
]}

Khurshudyan, Martiros ^{[3
]}

Myrzakulov, Kairat ^{[4
,5
]}

Zhadyranova, Aliya ^{[4
,5
]}

机构：

[1] Oakland Univ, Dept Phys, Rochester, MI 48309 USA

[2] Univ Garmian, Coll Educ, Dept Phys, Kalar, Kurdistan Regio, Iraq

[3] CSIC, ICE, IEEC, Campus UAB,Carrer Can Magrans s-n, Barcelona 08193, Spain

[4] Eurasian Natl Univ, Nur Sultan 010008, Kazakhstan

[5] Ratbay Myrzakulov Eurasian Int Ctr Theoret Phys, Nur Sultan 010009, Kazakhstan

来源：

EUROPEAN PHYSICAL JOURNAL C | 2022年 / 82卷 / 12期

关键词：

DARK; CONSTANT; DYNAMICS; MODELS;

D O I：

10.1140/epjc/s10052-022-11109-y

中图分类号：

O412 [相对论、场论]; O572.2 [粒子物理学];

学科分类号：

摘要：

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)=alpha T-0(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).

引用

页数：17

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