A comprehensive analysis of observational cosmology in f(Q) gravity with deep learning and MCMC method

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
L.K. Sharma , S. Parekh , A.K. Yadav , N. Goyal
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引用次数: 0

Abstract

Our goal in this study is to build FRW cosmological models inside the f(Q) theory of gravity framework by using Bayesian statistics and deep learning method. We investigate the universe’s accelerating behaviour for a specific version of the f(Q) gravity model using a novel, straightforward parameterization of the Hubble parameter in the form H=H0(1+z)1+q0q1exp(q1z). The corresponding free parameters in H(z) are limited between 1σ and 2σ confidence bounds using the χ2-minimization procedure. The results show that all the numbers we got are in the ballpark of what cosmological observations would predict. In our model, we examined the physical behaviour of the cosmos using characteristics such as energy density, pressure, and equation of state. We analysed kinematic factors including Hubble parameter, acceleration parameter, and universe age in our model. In our concept, the deceleration parameter q(z) represents the universe’s transition from deceleration to acceleration. We employ a novel approach for parameter estimation by utilizing a mixed neural network (MNN) that combines artificial neural networks (ANN) and mixture density networks (MDN). This new methodology leverages the strengths of ANN, MDN, and MNN to enhance the accuracy of parameter estimation.
用深度学习和 MCMC 方法全面分析 f(Q) 引力下的观测宇宙学
本研究的目标是利用贝叶斯统计和深度学习方法,在 f(Q) 引力理论框架内建立 FRW 宇宙学模型。我们使用一种新颖、直接的哈勃参数参数化形式 H=H0(1+z)1+q0-q1exp(q1z) 来研究特定版本的 f(Q) 引力模型的宇宙加速行为。利用 χ2 最小化程序将 H(z) 中相应的自由参数限制在 1σ 和 2σ 置信度之间。结果表明,我们得到的所有数字都与宇宙学观测所预测的相差无几。在我们的模型中,我们利用能量密度、压力和状态方程等特征研究了宇宙的物理行为。我们分析了模型中的哈勃参数、加速度参数和宇宙年龄等运动学因素。在我们的概念中,减速参数 q(z) 代表宇宙从减速到加速的过渡。我们利用混合神经网络(MNN)进行参数估计,这是一种结合了人工神经网络(ANN)和混合密度网络(MDN)的新方法。这种新方法充分利用了人工神经网络、MDN 和 MNN 的优势,提高了参数估计的准确性。
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来源期刊
Astronomy and Computing
Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍: Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.
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