论文标题
独立于模型无关校准伽马射线爆发的光度相关性，使用深度学习
Model-independently calibrating the luminosity correlations of gamma-ray bursts using deep learning
论文作者
论文摘要
在高红移时检测到的伽马射线爆发（GRB）可用于追踪宇宙的哈勃图。但是，GRB的距离校准不像IA型超新星（SNE IA）那样容易。对于基于经验光度相关性的校准方法，存在一个基本假设，即相关性在整个红移范围内应具有通用性。在本文中，我们研究了六个光度相关性的红移依赖性与完全独立于模型的深度学习方法。我们构建了一个结合了复发性神经网络（RNN）和贝叶斯神经网络（BNN）的网络，在该网络中，RNN用于通过通过Pantheon汇编训练网络来重建距离 - 红移关系，而BNN用于计算重建的不确定性。使用万神殿的重建距离红移关系，我们通过将完整的GRB样本分为两个子样本（低$ z $和高$ z $ samples）来测试六个光度相关性的红移依赖性，并发现只有$ e_p-e_γ$的关系没有证据表明redShift依赖性。我们使用$ e_p-e_γ$的关系来校准GRB，并且校准的GRB对flat $λ$ CDM型号进行了严格的约束，其中最合适的参数$ω_ {\ rm m} $ = 0.307 $^{+0.065} _ {+0.065} _ { - 0.073} $。
Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the Hubble diagram of the Universe. However, the distance calibration of GRBs is not as easily as that of type Ia supernovae (SNe Ia). For the calibrating method based on the empirical luminosity correlations, there is an underlying assumption that the correlations should be universal over the whole redshift range. In this paper, we investigate the possible redshift dependence of six luminosity correlations with a completely model-independent deep learning method. We construct a network combining the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), where RNN is used to reconstruct the distance-redshift relation by training the network with the Pantheon compilation, and BNN is used to calculate the uncertainty of the reconstruction. Using the reconstructed distance-redshift relation of Pantheon, we test the redshift dependence of six luminosity correlations by dividing the full GRB sample into two subsamples (low-$z$ and high-$z$ subsamples), and find that only the $E_p-E_γ$ relation has no evidence for redshift dependence. We use the $E_p-E_γ$ relation to calibrate GRBs, and the calibrated GRBs give tight constraint on the flat $Λ$CDM model, with the best-fitting parameter $Ω_{\rm M}$=0.307$^{+0.065}_{-0.073}$.
