论文标题
pyhiiextractor:从积分田间光谱数据中检测和提取HII区域的物理特性的工具
pyHIIextractor: A tool to detect and extract physical properties of HII regions from Integral Field Spectroscopic data
论文作者
论文摘要
我们提出了一个名为Pyhiiextractor的新代码,该代码使用Hα发射线图像检测并提取块状电离区域的主要特征(位置和半径),即候选HII区域。我们的代码被优化,可用于PIPE3D管道提供的数据核能(或具有此类格式的数据核能),应用于高空间分辨率积分集成谱数据(例如使用MUSE的有趣的++汇编)。该代码为每个检测到的H II候选者提供了基础恒星种群和排放线的特性。此外,该代码对弥散性离子气体(DIG)组件的新颖估计与其物理性质无关,从而使HII区域的性质从DIG中进行了净化。使用模拟数据,模仿螺旋星系的预期观测值,我们表征了pyhiiextractor及其提取H II区域(和DIG)的主要特性的能力,包括线通量,比率和等效宽度。最后,我们将代码与文献中采用的其他此类工具进行了比较,这些工具已被开发或用于类似目的:Pyhiiexplorer,source extractor,Hiiphot和Astrodendro。我们得出的结论是,pyhiiextractor超过了先前工具在诸如回收区域数量和大小和通量的分布等方面的性能(对于最微弱和最小的区域而言,这一点尤其引人注目)。因此,pyhiiextractor是检测候选HII区域的最佳工具,可以准确估算其性质和良好的挖掘成分去污染。
We present a new code named pyHIIextractor, which detects and extracts the main features (positions and radii) of clumpy ionized regions, i.e. candidate HII regions, using Hα emission line images. Our code is optimized to be used on the dataproducts provided by the Pipe3D pipeline (or dataproducts with such a format), applied to high spatial resolution Integral Field Spectroscopy data (like that provided by the AMUSING++ compilation, using MUSE). The code provides the properties of both the underlying stellar population and the emission lines for each detected H ii candidate. Furthermore, the code delivers a novel estimation of the diffuse ionized gas (DIG) component, independent of its physical properties, which enables a decontamination of the properties of the HII regions from the DIG. Using simulated data, mimicking the expected observations of spiral galaxies, we characterise pyHIIextractor and its ability to extract the main properties of the H ii regions (and the DIG), including the line fluxes, ratios and equivalent widths. Finally, we compare our code with other such tools adopted in the literature, which have been developed or used for similar purposes: pyhIIexplorer, SourceExtractor, HIIphot, and astrodendro. We conclude that pyHIIextractor exceeds the performance of previous tools in aspects such as the number of recovered regions and the distribution of sizes and fluxes (an improvement that is especially noticeable for the faintest and smallest regions). pyHIIextractor is therefore an optimals tool to detect candidate HII regions, offering an accurate estimation of their properties and a good decontamination of the DIG component.