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arXiv 2109.00636astro-ph.COgr-qc
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神经网络和标准宇宙学与新校准的高红移GRB观测

Neural networks and standard cosmography with newly calibrated high redshift GRB observations

Celia Escamilla-Rivera, Maryi Carvajal, Cristian Zamora, Martin Hendry

摘要:

高红移的伽马射线暴(GRB)可以用来追踪宇宙膨胀的历史。然而,与Ia型超新星(SNeIa)相比,校准它们的光度距离并不是一件容易的事情。为了校准这些数据,需要确定它们的亮度和其他观测到的伽玛暴性质之间的相关性,我们必须考虑我们关于这些相关性的假设在整个观测到的红移范围内的有效性。在这项工作中,我们提出了一种新的方法来校准伽玛暴作为宇宙学距离指标使用SNeIa观测与机器学习架构。此外,我们包括一个新的数据伽玛射线暴校准样品使用扩展宇宙学在红移范围以上$z>3.6$。在[1]中开发了这种机器学习技术的概述,以研究高红移下暗能量模型的演化。在这项工作中开发的方法的目的是结合两个网络:递归神经网络(RNN)和贝叶斯神经网络(BNN)。使用这种计算方法,表示为RNN+BNN,我们通过将协方差矩阵的计算添加到贝叶斯过程来扩展网络的功效。一旦这样做了,SNeIa距离红移关系可以在完整的伽玛暴样本上进行测试,因此可以用来实现不同区域的距离红移关系的宇宙学重建。因此,我们新训练的神经网络用于通过宇宙学方法在高红移(高达$z\approx 10$)下约束描述宇宙运动状态的参数,其中我们需要对深度学习架构本身进行非常少的假设,这些假设不依赖于任何特定引力理论的动力学方程。

英文摘要:

Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the cosmic expansion history. However, the calibration of their luminosity distances is not an easy task in comparison to Type Ia Supernovae (SNeIa). To calibrate these data, correlations between their luminosity and other observed properties of GRBs need to be identified, and we must consider the validity of our assumptions about these correlations over their entire observed redshift range. In this work, we propose a new method to calibrate GRBs as cosmological distance indicators using SNeIa observations with a machine learning architecture. As well we include a new data GRB calibrated sample using extended cosmography in a redshift range above $z>3.6$. An overview of this machine learning technique was developed in [1] to study the evolution of dark energy models at high redshift. The aim of the method developed in this work is to combine two networks: a Recurrent Neural Network (RNN) and a Bayesian Neural Network (BNN). Using this computational approach, denoted RNN+BNN, we extend the network's efficacy by adding the computation of covariance matrices to the Bayesian process. Once this is done, the SNeIa distance-redshift relation can be tested on the full GRB sample and therefore used to implement a cosmographic reconstruction of the distance-redshift relation in different regimes. Thus, our newly-trained neural network is used to constrain the parameters describing the kinematical state of the Universe via a cosmographic approach at high redshifts (up to $z\approx 10$), wherein we require a very minimal set of assumptions on the deep learning architecture itself that do not rely on dynamical equations for any specific theory of gravity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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