长伽马射线暴红移的估算
Estimating Redshifts for Long Gamma-Ray Bursts
摘要：
我们正在建立一个程序，从原始的雨燕光变曲线和光谱来估计伽玛暴的红移，旨在得到雨燕暴的红移\textit{without}光谱或光度红移。我们从每个爆发的光变曲线和光谱中推导出光度指标，包括低光子能量光变曲线和高光子能量光变曲线之间的滞后时间、光变曲线的可变性、光谱的峰值能量、光变曲线中的峰的数量以及峰的最小上升时间。这些光度指标可以直接与光度相关，我们将它们的独立光度合并为一个加权平均值。然后结合我们的光度值，观测到的爆发峰值亮度，以及一致性红移距离关系，我们可以推导出每个爆发的红移。在本文中，我们测试了我们的方法的准确性与已知的光谱红移的107爆发。与已知光谱红移（$z_{spec}$）相比，我们最好的红移（$z_{best}$）的约化$χ^2$为0.86，$log_{10}(z_{best}/z_{spec})$的平均值为0.01，这表明我们的误差棒很好，我们的估计没有偏差。$log_{10}(z_{best}/z_{spec})$的RMS离散度为0.26。对于在相对窄的能带上测量的雨燕爆发，确定峰值能量的不确定性是我们精度的主要限制之一。虽然我们的$z_{best}$值的准确性不如光谱红移，但它对人口统计学研究非常有用，因为我们的样本几乎是完整的，而且红移不具有与光谱学相关的严重选择效应。
英文摘要：
We are constructing a program to estimate the redshifts for GRBs from the original Swift light curves and spectra, aiming to get redshifts for the Swift bursts \textit{without} spectroscopic or photometric redshifts. We derive the luminosity indicators from the light curves and spectra of each burst, including the lag time between low and high photon energy light curves, the variability of the light curve, the peak energy of the spectrum, the number of peaks in the light curve, and the minimum rise time of the peaks. These luminosity indicators can each be related directly to the luminosity, and we combine their independent luminosities into one weighted average. Then with our combined luminosity value, the observed burst peak brightness, and the concordance redshift-distance relation, we can derive the redshift for each burst. In this paper, we test the accuracy of our method on 107 bursts with known spectroscopic redshift. The reduced $χ^2$ of our best redshifts ($z_{best}$) compared with known spectroscopic redshifts ($z_{spec}$) is 0.86, and the average value of $log_{10}(z_{best}/z_{spec})$ is 0.01, with this indicating that our error bars are good and our estimates are not biased. The RMS scatter of $log_{10}(z_{best}/z_{spec})$ is 0.26. For Swift bursts measured over a relatively narrow energy band, the uncertainty in determining the peak energy is one of the main restrictions on our accuracy. Although the accuracy of our $z_{best}$ values are not as good as that of spectroscopic redshifts, it is very useful for demographic studies, as our sample is nearly complete and the redshifts do not have the severe selection effects associated with optical spectroscopy.
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