AutoProf -- I.用于现代星系巡天的自动非参量光剖面管道
AutoProf -- I. An automated non-parametric light profile pipeline for modern galaxy surveys
摘要：
我们提出了一个自动化的非参数光轮廓提取管道称为AutoProf。提取表面亮度（SB）轮廓的所有步骤都包含在AutoProf中，允许对星系图像进行简化分析。AutoProf改进了以前的非参数椭圆拟合实现，其拟合稳定程序改编自机器学习技术。灵活的管道中还包括其他高级分析方法，用于提取替代亮度剖面（沿径向或轴向切片）、平滑轴对称模型，以及为任意复杂的管道实施决策树。与广泛使用的测光算法（photutils，XVISTA和GALFIT）的详细比较。这些比较依赖于从PROBES巡天中收集的大量晚型星系图像。SB剖面的直接比较表明，AutoProf可以在相同的图像上可靠地提取出比其他方法更暗的等照度线，通常大于2 mag/arcsec^2。将非参数椭圆等照度线拟合与简单参数模型进行对比还表明，双分量拟合（例如，Sérsic加指数）不足以描述具有高保真度的晚型星系。椭圆等照度线拟合，特别是AutoProf，非常适合广泛的自动等照度线分析任务。AutoProf可在以下网址免费向社区提供：https://github.com/ConnorStoneAstro/AutoProf
英文摘要：
We present an automated non-parametric light profile extraction pipeline called AutoProf. All steps for extracting surface brightness (SB) profiles are included in AutoProf, allowing streamlined analyses of galaxy images. AutoProf improves upon previous non-parametric ellipse fitting implementations with fit-stabilization procedures adapted from machine learning techniques. Additional advanced analysis methods are included in the flexible pipeline for the extraction of alternative brightness profiles (along radial or axial slices), smooth axisymmetric models, and the implementation of decision trees for arbitrarily complex pipelines. Detailed comparisons with widely used photometry algorithms (photutils, XVISTA, and GALFIT) are presented. These comparisons rely on a large collection of late-type galaxy images from the PROBES survey. The direct comparison of SB profiles shows that AutoProf can reliably extract fainter isophotes than other methods on the same images, typically by >2 mag/arcsec^2. Contrasting non-parametric elliptical isophote fitting with simple parametric models also shows that two-component fits (e.g., Sérsic plus exponential) are insufficient to describe late-type galaxies with high fidelity. It is established that elliptical isophote fitting, and in particular AutoProf, is ideally suited for a broad range of automated isophotal analysis tasks. AutoProf is freely available to the community at: https://github.com/ConnorStoneAstro/AutoProf
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
