论文标题
通过贝叶斯机器学习解决$ h_ {0} $张力在$ f（t）$重力
Solving the $H_{0}$ tension in $f(T)$ Gravity through Bayesian Machine Learning
论文作者
论文摘要
贝叶斯机器学习〜（BML）和强镜时间延迟〜（SLTD）技术用于应对$ h_ {0} $张力在$ f（t）$ gravity中。 BML的力量依赖于采用基于模型的生成过程，该过程已经在宇宙学和天体物理学的不同领域中起着重要作用，这是目前的工作。考虑了三个可行的$ f（t）$模型：一个电源法，指数和平方指数模型。学到的约束和相应的结果表明，指数模型$ f（t）=αT_{0} \ left（1-e^{ - p t / t / t_ {0}}} \ right）$，具有求解$ h_ {0} $张力的能力。通过考虑涉及的镜头和源的不同红移范围和参数来显示该方法的预测能力和鲁棒性。所学到的是，这些值可以强烈影响我们对$ h_ {0} $张力的理解，就像在考虑的模型的情况下确实发生了。最终使用观察性哈勃数据（OHD）验证了学习方法的最终约束。
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, $f(T)=αT_{0}\left(1-e^{-p T / T_{0}}\right)$, 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).
