利用机器学习集成模型推断超过150个GRB的红移
Inferring the redshift of more than 150 GRBs with a Machine Learning Ensemble model
AI总结：
针对仅11%伽马射线暴已知红移的观测限制，提出基于X射线余辉的集成监督机器学习模型估计红移，推断154个GRB红移使已知数量增加94%，均方根误差0.46，预测提升63%。
摘要：
伽马射线暴（GRBs）由于其高光度，可被探测至红移10，因此有望成为研究宇宙早期过程的重要宇宙学探针。要发挥这一潜力，需要大量已知红移的GRB样本，但由于观测限制，仅有11%的GRB已知红移（$z$）。此前已有许多通过相关性研究估计红移的尝试，但大多导致预测不准确。为克服此问题，我们利用一个集成监督机器学习模型，基于Neil Gehrels Swift Observatory观测的长持续时间GRB的X射线余辉来估计GRB红移。估计的红移与观测红移高度相关（Pearson系数为0.93），均方根误差，即平均平方误差$\langleΔz^2\rangle$的平方根为0.46，表明了该方法的可靠性。与同行评议文献中的先前结果相比，加入GRB余辉参数使预测显著提升了63%。最后，我们使用该机器学习模型推断出154个GRB的红移，使具有平台期的长GRB已知红移数量增加了94%，这是推动需要大样本红移的GRB族群研究的重要里程碑。
英文摘要：
Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up to redshift 10, and thus have the potential to be vital cosmological probes of early processes in the universe. Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11\% have known redshifts ($z$). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble supervised machine learning model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have a root mean square error, namely the square root of the average squared error $\langleΔz^2\rangle$, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63\% compared to previous results in peer-reviewed literature. Finally, we use our machine learning model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94\%, a significant milestone for enhancing GRB population studies that require large samples with redshift.
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