发表机构
Pulkovo Astronomical Observatory, Russian Academy of Sciences; Sternberg Astronomical Institute, Lomonosov Moscow State University（俄罗斯科学院普尔科沃天文台; 罗蒙诺索夫莫斯科国立大学施滕贝格天文研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍IMFIT-MW，一个开源Python工具，扩展IMFIT以同时建模多波段图像，支持共享参数和波长平滑变化，性能与GALFITM相当，并展示无需修改源码即可扩展功能。
AI 中文摘要
光度分解通常通过将表面亮度分布建模为其组成部分来研究星系结构。然而，大多数相关代码都是为单波段分析设计的。我介绍了IMFIT-MW：一个轻量级、开源的Python工具，它将IMFIT的功能扩展为同时建模多波段图像。IMFIT-MW能够在不同滤光片之间设置共享参数，并强制这些参数随波长平滑变化。IMFIT-MW通过使用IMFIT为给定参数生成模型亮度分布来实现这一点，而定义其波长依赖性的超参数则由外部优化器拟合。该代码可在GitHub上获取（此https URL）。我提供了一些使用示例，表明IMFIT-MW在单线程性能上与GALFITM相当，并且可以利用多线程的优势。从更广泛的意义上讲，我展示了即使不修改IMFIT源代码也能扩展其功能的可能性。
英文摘要
Photometric decomposition is often used to study galaxy structure by modelling surface brightness distribution in its components. However, most of the relevant codes are designed for single-band analysis. I present IMFIT-MW: a lightweight, open source Python tool that extends IMFIT capabilities to simultaneously model multiwavelength images. IMFIT-MW is able to set shared parameters between filters and to impose smooth variation of them with wavelength. IMFIT-MW achieves this by using IMFIT to produce model brightness distributions for given parameters, whereas hyperparameters defining their wavelength dependence are fitted by an external optimizer. The code is available on GitHub (https://github.com/IVChugunov/IMFIT-MW). I provide a few examples of use, showing that IMFIT-MW matches GALFITM in single-thread performance and can have advantage of multithreading. In a broader sense, I demonstrate the possibility to extend IMFIT features even without modifying its source code.
CommentsAccepted to Astronomy & Computing. Code is available at https://github.com/IVChugunov/IMFIT-MW
