通过非参数指标和机器学习方法的形态学研究DECaLS星系：使用Galaxy Morphology Extractor（galmex）代码的综合目录
Morphologies for DECaLS Galaxies through a combination of non-parametric indices and machine learning methods: A comprehensive catalog using the Galaxy Morphology Extractor (galmex) code
AI总结：
本文利用非参数指标和机器学习方法，构建DECaLS星系的形态学目录，评估指标分离螺旋星系和椭圆星系的可靠性，并测试其与现有分类方案的一致性。
摘要：
星系形态学编码了关于形成和演化的关键信息。大型成像调查需要超越视觉检查的自动化、可重复的方法。非参数指标提供了一个有用的框架，但其性能必须进行定量评估。我们为DECaLS中有效半径大于2弧秒的星系提供了同质的非参数形态学指标目录。我们的目标是评估指标在分离螺旋星系和椭圆星系中的可靠性，测试其与现有分类方案的一致性，并建立其在即将进行的南半球调查中的适用性。我们开发了galmex，一个用于图像预处理和测量多种非参数指标的模块化Python包。使用 bona-fide 螺旋星系和椭圆星系作为对照样本，我们评估了每个指标的判别能力，并将其与基于CNN的T-Types和Galaxy Zoo DECaLS标签进行了比较。我们使用这些指标作为输入，通过Light Gradient Boosted Machine（LightGBM）获得概率分类。浓度是CAS系统（浓度+不对称性+平滑性）中最可靠的参数，而基于不对称性的指标（A和S）仅限于检测扰动形态。MEGG指标（M20、熵、Gini、G2）提供更强的分离能力，并显示出与T-Types的渐变关系。通过使用简单的二元（0/1）标签对椭圆星系/螺旋星系进行分类，基于非参数指标训练的分类器在非参数指标上实现了高准确性和良好的校准概率，主要由熵、浓度和Gini主导。我们发布了DECaLS的首个公共CA[A_S]S+MEGG指标目录，以及galmex。我们结合非参数指标和机器学习框架，通过概率方法推导出z~0.15以下星系的螺旋/椭圆分离。
英文摘要：
Galaxy morphology encodes key information about formation and evolution. Large imaging surveys require automated, reproducible methods beyond visual inspection. Non--parametric indices provide an useful framework, but their performance must be quantitatively assessed. We present a homogeneous catalog of non--parametric morphological indices for DECaLS galaxies with effective radii larger than 2 arcsec. Our goal is to evaluate the reliability of indices in separating spirals and ellipticals, test their consistency with existing classification schemes, and establish their applicability for the upcoming surveys focused in the southern hemisphere. We developed galmex, a modular Python package for preprocessing images and measuring a variety of non--parametric indices. Using bona-fide spirals and ellipticals as control samples, we assessed the discriminatory power of each index, and compared them with CNN-based T-Types and Galaxy Zoo DECaLS labels. We use the indices as input for a Light Gradient Boosted Machine (LightGBM) to obtain probabilistic classifications. Concentration is the most reliable parameter from the Concentratiom + Asymmetry + Smoothness system (CAS), while asymmetry--based indices (A and S) are limited to detecting disturbed morphologies. MEGG indices (M20, Entropy, Gini, G2) provide stronger separation and trace a gradient with T--Type. By using a simple binary (0/1) label for ellipticals/spirals, classifiers trained on non--parametric indices achieve high accuracy and well--calibrated probabilities, dominated by entropy, concentration, and Gini. We release the first public catalog of CA[A_S]S+MEGG indices for DECaLS, together with galmex. We combine the non-parametric indices with machine learning framework to derive spiral/elliptical separation for galaxies below z~0.15 through a probabilistic approach.
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