Physics of the Dark Universe ( IF 5.0 ) Pub Date : 2021-04-18 , DOI: 10.1016/j.dark.2021.100820 Raja Solanki , S.K.J. Pacif , Abhishek Parida , P.K. Sahoo
In this article, we have investigated the role of bulk viscosity to study the accelerated expansion of the universe in the framework of modified gravity. The gravitational action in this modified gravity theory has the form , where denote the non-metricity scalar. In the present manuscript, we have considered a bulk viscous matter-dominated cosmological model with the bulk viscosity coefficient of the form which is proportional to the velocity and acceleration of the expanding universe. Two sets of limiting conditions on the bulk viscous parameters and model parameter arose here out of which one condition favors the present scenario of cosmic acceleration with a phase transition and corresponds to the universe with a Big Bang origin. Moreover, we have discussed the cosmological behavior of some geometrical parameters. Then, we have obtained the best fitting values of the model parameters and by constraining our model with updated Hubble datasets consisting of 57 data points and recently released Pantheon datasets consisting of 1048 data points which show that our obtained model has good compatibility with observations. Further, we have also included the Baryon Acoustic Oscillation (BAO) datasets of six data points with the Hubble & Pantheon datasets and obtained slightly different values of the model parameters. Finally, we have analyzed our model with the statefinder diagnostic analysis and found some interesting results and are discussed in details.
中文翻译:
改性后的体积黏性宇宙加速 重力
在本文中,我们研究了本体粘度在改性框架下研究宇宙加速膨胀的作用。 重力。这个修正的重力理论中的重力作用具有以下形式:, 在哪里 表示非度量标量。在本手稿中,我们考虑了以体积粘滞物质为主的宇宙学模型,其体积粘滞系数为这与膨胀宇宙的速度和加速度成正比。两组对粘性参数的限制条件 和模型参数 在这里出现了一种情况,其中一种条件有利于当前具有相变的宇宙加速场景,并且对应于起源于大爆炸的宇宙。此外,我们讨论了一些几何参数的宇宙学行为。然后,我们获得了模型参数的最佳拟合值 和 通过用包含57个数据点的更新的哈勃数据集和包含1048个数据点的最新发布的Pantheon数据集对我们的模型进行约束,这表明我们获得的模型与观测值具有良好的兼容性。此外,我们还将六个数据点的重子声振荡(BAO)数据集与Hubble&Pantheon数据集包括在内,并获得了略有不同的模型参数值。最后,我们使用statefinder诊断分析对模型进行了分析,发现了一些有趣的结果,并进行了详细讨论。