发表机构
Université Paris Cité; Miranda House, University of Delhi（巴黎西岱大学; 德里大学米兰达学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用贝叶斯神经网络（BNNs）结合观测哈勃数据（OHD）与Pantheon+超新星样本，重新校准GRB的二维、三维戴诺蒂相关性，发现三维相关性散度更低，Pantheon+校准效果更优，为GRB不依赖模型的校准提供了稳健框架。
AI 中文摘要
伽马射线暴（GRBs）是极具潜力的宇宙学探针，但作为标准烛光的应用受限于循环问题，因此需要对GRB光度相关性进行不依赖模型的校准。我们利用在更新后的观测哈勃数据（OHD）和Pantheon+ Ia型超新星样本上训练的贝叶斯神经网络（BNNs），重新审视二维（2D）和三维（3D）戴诺蒂（Dainotti）相关性。重构的光度距离用于校准Platinum和Narendra等人的GRB样本。我们约束了二维戴诺蒂关系和三维基本平面的参数，并考察了校准数据集与GRB样本选择的影响。使用Pantheon+进行的校准比OHD能得到更严格的约束，而三维相关性的本征散度低于二维关系。我们的结果表明，BNNs为GRB光度相关性的不依赖模型校准提供了稳健框架，且具有可靠的不确定性传播能力。此外，基础距离探针是不依赖模型校准的关键因素，决定了GRB样本的规模和所得约束的精度。
英文摘要
Gamma-ray bursts (GRBs) are promising cosmological probes, but their use as standard candles is limited by the circularity problem, necessitating model-independent calibration of GRB luminosity correlations. We revisit the two-dimensional (2D) and three-dimensional (3D) Dainotti correlations using Bayesian Neural Networks (BNNs) trained on the updated Observational Hubble Data (OHD) and Pantheon+ Type Ia Supernova sample. The reconstructed luminosity distances are used to calibrate the Platinum and Narendra et al. GRB samples. We constrain the parameters of the 2D Dainotti relation and the 3D fundamental plane, and examine the impact of calibration datasets and GRB sample selection. Calibration achieved using Pantheon+ yields tighter constraints than OHD, while the 3D correlation exhibits lower intrinsic scatter than the 2D relation. Our results demonstrate that BNNs provide a robust framework for model independent calibration of GRB luminosity correlations with reliable uncertainty propagation. Further, the underlying distance probe is a key factor in model-independent calibration, determining both the size of the GRB samples and the precision of the resulting constraints.
Comments24 pages, 5 Tables, 7 Figures; comments are welcomed
