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arXiv 2401.11005astro-ph.HEastro-ph.COastro-ph.IM
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用于估计伽马射线暴红移的深度神经网络

Deep Neural Networks for Estimation of Gamma-Ray Burst Redshifts

Tamador Aldowma, Soebur Razzaque

AI总结:

针对无红移的伽马射线暴数据,利用基于光谱参数的深度神经网络与随机森林模型估计红移,发现其能合理重现伪红移分布并满足Amati与Yonetoku关系。

摘要:

尽管目前已知红移的伽马射线暴(GRB)数据集有限,但不同仪器提供了大量无红移的GRB数据。这些数据包含精确测量的瞬时伽马射线流量和光谱信息。我们利用基于光谱参数的机器学习技术,估计了由Fermi-GBM和Konus-Wind探测到的一部分GRB的红移。我们发现,采用输入参数间非线性关系的深度神经网络与随机森林模型,能够合理地重现GRB的伪红移分布,模拟出具有光谱红移的GRB分布。此外,我们发现这些GRB的伪红移样本满足:(i) GRB宇宙学静止系中时间平均能谱的峰值光子能量 ${E}_{\rm i, p}$ 与瞬时阶段各向同性等效辐射能量 ${E}_{\rm iso}$ 之间的 Amati 关系;以及 (ii) ${E}_{\rm i, p}$ 与各向同性等效光度 ${L}_{\rm iso}$ 之间的 Yonetoku 关系,两者均在峰值流量间隔内测量。

英文摘要:

While the available set of Gamma-ray Burst (GRB) data with known redshift is currently limited, a much larger set of GRB data without redshift is available from different instruments. This data includes well-measured prompt gamma-ray flux and spectral information. We estimate the redshift of a selection of these GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques that are based on spectral parameters. We find that Deep Neural Networks with Random Forest models employing non-linear relations among input parameters can reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the distribution of GRBs with spectroscopic redshift. Furthermore, we find that the pseudo-redshift samples of GRBs satisfy (i) Amati relation between the peak photon energy of the time-averaged energy spectrum in the cosmological rest frame of the GRB ${E}_{\rm i, p}$ and the isotropic-equivalent radiated energy ${E}_{\rm iso}$ during the prompt phase; and (ii) Yonetoku relation between ${E}_{\rm i, p}$ and isotropic-equivalent luminosity ${L}_{\rm iso}$, both measured during the peak flux interval.

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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