调整目标选择算法以改进星系红移估计
Tuning target selection algorithms to improve galaxy redshift estimates
摘要：
我们展示了机器学习（ML）启发的目标选择算法，以确定应首先选择所有潜在目标中的哪一个进行光谱跟踪。有效的目标选择可以改善ML红移的不确定性计算的一个独立的样本，同时需要更少的目标被观察到。我们比较了ML的目标定位算法与斯隆数字巡天（SDSS）的目标顺序，并与随机定位算法。ML启发的算法是通过估计剩余目标星系中的哪一个将是机器学习方法使用先前观测到的数据准确估计红移最困难的来迭代构建的。这是通过预测所有剩余目标星系的预期红移误差和红移偏移（或偏差）来执行的。我们发现，即使只有有限的训练样本量，预测值的偏差和误差是准确的，优于10-30%的真实值。我们构建了一个假设的后续调查，发现一些ML目标定位算法能够获得相同的红移预测能力，与SDSS或随机目标选择算法相比，观测时间减少2-3倍。所需后续资源的减少可以允许改变后续策略，例如通过获得更深的光谱学，这可以改善更深测试数据的ML红移估计。
英文摘要：
We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as calculated on an independent sample, while requiring less targets to be observed. We compare the ML targeting algorithms with the Sloan Digital Sky Survey (SDSS) target order, and with a random targeting algorithm. The ML inspired algorithms are constructed iteratively by estimating which of the remaining target galaxies will be most difficult for the machine learning methods to accurately estimate redshifts using the previously observed data. This is performed by predicting the expected redshift error and redshift offset (or bias) of all of the remaining target galaxies. We find that the predicted values of bias and error are accurate to better than 10-30% of the true values, even with only limited training sample sizes. We construct a hypothetical follow-up survey and find that some of the ML targeting algorithms are able to obtain the same redshift predictive power with 2-3 times less observing time, as compared to that of the SDSS, or random, target selection algorithms. The reduction in the required follow up resources could allow for a change to the follow-up strategy, for example by obtaining deeper spectroscopy, which could improve ML redshift estimates for deeper test data.
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