标题： 一种基于主动实例的机器学习方法在恒星群体研究中的应用 显示英文标题

标题： An Active Instance-based Machine Learning method for Stellar Population Studies

摘要： 我们开发了一种快速且准确确定恒星群体参数的方法，以便将其应用于高分辨率星系光谱。 该方法基于一种优化技术，结合了主动学习和基于实例的机器学习算法。 我们用这种方法来检索“合成”星系的恒星形成历史和尘埃含量，这些星系具有广泛的信噪比（S/N）范围。 这些“合成”星系是使用两种不同的高分辨率理论人口综合模型网格构建的。 我们受控实验的结果表明，即使对于非常低的信噪比输入，我们的方法也能以良好的速度和准确性估计构成星系的恒星群体参数。 对于信噪比 S/N=5 的光谱，输入光谱与拟合光谱之间的典型平均偏差小于 10**{-5}。 使用先验知识可以实现进一步改进。 显示英文摘要

摘要： We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an instance-based machine learning algorithm. We tested the method with the retrieval of the star-formation history and dust content in "synthetic" galaxies with a wide range of S/N ratios. The "synthetic" galaxies where constructed using two different grids of high resolution theoretical population synthesis models. The results of our controlled experiment shows that our method can estimate with good speed and accuracy the parameters of the stellar populations that make up the galaxy even for very low S/N input. For a spectrum with S/N=5 the typical average deviation between the input and fitted spectrum is less than 10**{-5}. Additional improvements are achieved using prior knowledge.