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arXiv 2402.04551astro-ph.HEastro-ph.COastro-ph.IM
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基于统计学习方法的伽马射线暴作为距离指示器

Gamma-ray Bursts as Distance Indicators by a Statistical Learning Approach

Maria Giovanna Dainotti, Aditya Narendra, Agnieszka Pollo, Vahe Petrosian, Malgorzata Bogdan, Kazunari Iwasaki, Jason Xavier Prochaska, Enrico Rinaldi, David Zhou

AI总结:

本文利用有监督统计学习模型,通过 Swift 和地面望远镜观测的光学余辉估计伽马射线暴红移,推断红移与观测红移相关系数达 0.93,并据此估算 GRB 发生率,发现其与恒星形成率在低红移处存在 3 倍差异。

摘要:

伽马射线暴(GRBs)可以作为早期宇宙的探针,但由于观测限制,目前 Neil Gehrels Swift 天文台观测到的 GRBs 中只有 26% 具有已知红移($z$)。为了解决这一问题,我们利用 Swift 和地面望远镜观测到的光学余辉,通过有监督统计学习模型估计了 GRB 的红移(距离)。推断出的红移与观测红移之间存在很强的相关性(皮尔逊相关系数为 0.93),从而证明了该方法的可靠性。利用推断红移和观测红移,我们估计在给定红移处发生的 GRB 数量(GRB 发生率)在 $1.9<z<2.3$ 范围内为 8.47-9 $yr^{-1} Gpc^{-1}$。由于 GRB 来自大质量恒星的坍缩,我们将该发生率与恒星形成率进行了比较,发现在 $z<1$ 处存在 3 倍的差异。

英文摘要:

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts ($z$) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 $yr^{-1} Gpc^{-1}$ for $1.9<z<2.3$. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate highlighting a discrepancy of a factor of 3 at $z<1$.

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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