Mr-Moose：一种用于异构多波长数据集的高级SED拟合工具
Mr-Moose: An advanced SED-fitting tool for heterogeneous multi-wavelength datasets
摘要：
我们提出了MrMoose的公开发布，MrMoose是一种能够在贝叶斯框架中执行多波长和多目标光谱能量分布（SED）拟合的拟合程序。该过程能够处理各种各样的情况，从孤立源到来自异质数据集的混合多分量源（即，一系列观测灵敏度和光谱/空间分辨率）。此外，MrMoose在拟合过程中以连续的方式处理上限，允许模型随着接近上限而逐渐降低可能性。其目的是提出一种简单易用，但高度通用的拟合工具，用于处理多波长数据集与完全可定制的滤波器/模型数据库相结合时不断增加的源复杂性。用户的完全控制是一个优点，其避免了与“黑盒”效应相关的传统问题，其中参数或模型调谐是不可能的并且可能导致结果的过度拟合和/或过度解释。此外，虽然需要Python和统计学的基本知识，但代码旨在对非专家足够用户友好。我们展示了三种情况下的程序：两个人工生成的数据集和以前的结果从文献。特别是，最复杂的情况下（灵感来自一个真正的来源，结合赫歇尔，ALMA和VLA数据）在河外SED拟合的背景下，使MrMoose一个特别有吸引力的SED拟合工具时，处理部分混合源，而不需要数据反卷积。
英文摘要：
We present the public release of MrMoose, a fitting procedure that is able to perform multi-wavelength and multi-object spectral energy distribution (SED) fitting in a Bayesian framework. This procedure is able to handle a large variety of cases, from an isolated source to blended multi-component sources from an heterogeneous dataset (i.e. a range of observation sensitivities and spectral/spatial resolutions). Furthermore, MrMoose handles upper-limits during the fitting process in a continuous way allowing models to be gradually less probable as upper limits are approached. The aim is to propose a simple-to-use, yet highly-versatile fitting tool fro handling increasing source complexity when combining multi-wavelength datasets with fully customisable filter/model databases. The complete control of the user is one advantage, which avoids the traditional problems related to the "black box" effect, where parameter or model tunings are impossible and can lead to overfitting and/or over-interpretation of the results. Also, while a basic knowledge of Python and statistics is required, the code aims to be sufficiently user-friendly for non-experts. We demonstrate the procedure on three cases: two artificially-generated datasets and a previous result from the literature. In particular, the most complex case (inspired by a real source, combining Herschel, ALMA and VLA data) in the context of extragalactic SED fitting, makes MrMoose a particularly-attractive SED fitting tool when dealing with partially blended sources, without the need for data deconvolution.
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。
