标题： 基于随机森林的ALHAMBRA巡天中类星体的测光分类 显示英文标题

标题： Photometric classification of QSOs from ALHAMBRA survey using random forest

摘要： 上下文：在当前的大数据时代，天文学中近年来已开始应用基于机器学习的方法，从全天空的光度巡天数据中识别或分类类星体、星系和恒星等天体。 目标：在这里，我们系统地评估随机森林（RF）在使用亮度或颜色作为特征时，对类星体分类的性能，这些特征来自宽波段和窄波段滤镜。 方法：工作数据包括来自ALHAMBRA金目录的光度数据，我们将其与斯隆数字天空调查（SDSS）和百万类星体目录（Milliquas）进行了交叉匹配，以获取被标记为类星体、星系或恒星的对象。 训练并测试了一个RF分类器，以评估改变自由参数对最终准确率和精确度的影响，以及使用窄波段或宽波段亮度或颜色的影响。 结果：分类器的最佳表现达到了约0.9的全局准确率和类星体精确度。 在合理值范围内改变模型自由参数对最终分类没有显著影响。 使用颜色而不是亮度作为特征可以提高分类器的性能，特别是使用来自ALHAMBRA调查的颜色。 对分类贡献最大的颜色是包含近红外$JHK$波段的颜色。 显示英文摘要

摘要： Context: Given the current big data era in Astronomy, machine learning based methods have being applied over the last years to identify or classify objects like quasars, galaxies and stars from full sky photometric surveys. Aims: Here we systematically evaluate the performance of Random Forests (RF) in classifying quasars using either magnitudes or colours, both from broad and narrow-band filters, as features. Methods: The working data consists of photometry from the ALHAMBRA Gold Catalogue that we cross-matched with the Sloan Digital Sky Survey (SDSS) and with the Million Quasars Catalogue (Milliquas) for objects labelled as quasars, galaxies or stars. A RF classifier is trained and tested to evaluate the effect on final accuracy and precision of varying the free parameters and the effect of using narrow or broad-band magnitudes or colours. Results: Best performances of the classifier yielded global accuracy and quasar precision around 0.9. Varying model free parameters (within reasonable ranges of values) has no significant effects on the final classification. Using colours instead of magnitudes as features results in better performances of the classifier, especially using colours from the ALHAMBRA Survey. Colours that contribute the most to the classification are those containing the near-infrared $JHK$ bands.