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arXiv 1907.05074astro-ph.HE
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通过机器学习方法将伽马射线暴作为距离指示器

Gamma-ray Bursts as distance indicators through a machine learning approach

Maria Dainotti, Vahé Petrosian, Malgorzata Bogdan, Blazej Miasojedow, Shigehiro Nagataki, Trevor Hastie, Zooey Nuyngen, Sankalp Gilda, Xavier Hernandez, Dominika Krol

摘要:

伽马射线暴(GRB)是一种高能事件,一旦知道它们的红移,就有可能为早期宇宙及其演化提供信息。不幸的是,确定红移是一个艰苦的过程,需要详细的后续多波长观测,通常涉及各种天文设施,这些设施必须迅速指向这些偶然事件。在这里,我们使用机器学习算法来推断红移从一组观测到的时间和光谱特征的伽玛暴。我们得到了一个非常高的相关系数($0.96$)之间的推断和观察到的红移,和一个小的分散(均方误差为$0.003$)在测试集。平台余辉参数的加入改善了$61.4\%$的预测相比,以前的结果。从预测和观测到的红移中得到的GRB光度函数和累积密度率演化非常一致,表明GRB是有效的距离指示器和宇宙距离阶梯的可靠步骤。

英文摘要:

Gamma-ray bursts (GRBs) are spectacularly energetic events, with the potential to inform on the early universe and its evolution, once their redshifts are known. Unfortunately, determining redshifts is a painstaking procedure requiring detailed follow-up multi-wavelength observations often involving various astronomical facilities, which have to be rapidly pointed at these serendipitous events. Here we use Machine Learning algorithms to infer redshifts from a collection of observed temporal and spectral features of GRBs. We obtained a very high correlation coefficient ($0.96$) between the inferred and the observed redshifts, and a small dispersion (with a mean square error of $0.003$) in the test set. The addition of plateau afterglow parameters improves the predictions by $61.4\%$ compared to previous results. The GRB luminosity function and cumulative density rate evolutions, obtained from predicted and observed redshift are in excellent agreement indicating that GRBs are effective distance indicators and a reliable step for the cosmic distance ladder.

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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