miniJPAS调查：使用机器学习进行恒星-星系分类
The miniJPAS survey: star-galaxy classification using machine learning
摘要：
未来的天体物理调查，如J-PAS，将产生非常大的数据集，这将需要部署准确和有效的机器学习（ML）方法。在这项工作中，我们分析了miniJPAS调查，观察约1度2的AEGIS领域与56窄带滤波器和4 ugri宽带滤波器。我们讨论了分类的miniJPAS源扩展（星系）和点状（如恒星）的对象，后续的科学分析的必要步骤。我们的目标是开发一个ML分类器，它是基于显式建模的传统工具的补充。为了训练和测试我们的分类器，我们将miniJPAS数据集与SDSS和HSC-SSP数据交叉匹配。我们在两个交叉匹配的目录上训练和测试了6种不同的ML算法。作为ML算法的输入，我们使用60个滤波器的幅度及其误差，有和没有形态参数。我们还使用每个指向的r检测频带中的平均PSF。我们发现，RF和ERT算法在所有情况下表现最好。当分析15<r<23.5的全幅度范围时，我们发现当仅使用光度信息时RF的AUC=0.957，并且当使用光度和形态信息时ERT的AUC=0.986。关于特征重要性，当使用形态学参数时，FWHM是最重要的特征。当仅使用光度信息时，我们观察到宽带不一定比窄带更重要，并且误差与测量值一样重要。ML算法可以与传统的恒星/星系分类器竞争，在较暗的星等（r>21）上优于后者。我们使用我们最好的分类器，有和没有形态学，以产生一个增值的目录，可在https://j-pas.org/datareleases。
英文摘要：
Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 15<r<23.5 we find AUC=0.957 with RF when using only photometric information, and AUC=0.986 with ERT when using photometric and morphological information. Regarding feature importance, when using morphological parameters, FWHM is the most important feature. When using photometric information only, we observe that broad bands are not necessarily more important than narrow bands, and errors are as important as the measurements. ML algorithms can compete with traditional star/galaxy classifiers, outperforming the latter at fainter magnitudes (r>21). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases .
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