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Three-Dimensional Optimal Spectral Extraction (TDOSE) from integral field spectroscopy ?

机译:从积分场光谱学中提取三维最佳光谱(TDOSE)

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The amount of integral field spectrograph (IFS) data has grown considerably over the last few decades. The demand for tools to analyze such data is therefore bigger now than ever. We present a flexible Python tool for Three-Dimensional Optimal Spectral Extraction (TDOSE) from IFS data cubes. TDOSE works on any three-dimensional data cube and bases the spectral extractions on morphological reference image models. By default, these models are generated and composed of multiple multivariate Gaussian components, but can also be constructed with independent modeling tools and be provided as input to TDOSE. In each wavelength layer of the IFS data cube, TDOSE simultaneously optimizes all sources in the morphological model to minimize the difference between the scaled model components and the IFS data. The flux optimization produces individual data cubes containing the scaled three-dimensional source models. This allows the efficient de-blending of flux in both the spatial and spectral dimensions of the IFS data cubes, and extraction of the corresponding one-dimensional spectra. TDOSE implicitly requires an assumption about the two-dimensional light distribution. We describe how the flexibility of TDOSE can be used to mitigate and correct for deviations from the input distribution. Furthermore, we present an example of how the three-dimensional source models generated by TDOSE can be used to improve two-dimensional maps of physical parameters like velocity, metallicity, or star formation rate when flux contamination is a problem. By extracting TDOSE spectra of ~150 [OII] emitters from the MUSE-Wide survey we show that the median increase in line flux is ~5% when using multi-component models as opposed to single-component models. However, the increase in recovered line emission in individual cases can be as much as 50%. Comparing the TDOSE model-based extractions of the MUSE-Wide [OII] emitters with aperture spectra, the TDOSE spectra provides a median flux (S/N) increase of 9% (14%). Hence, TDOSE spectra optimize the S/N while still being able to recover the total emitted flux.
机译:在过去的几十年中,积分场光谱仪(IFS)数据的数量已大大增加。因此,如今对于分析此类数据的工具的需求比以往任何时候都大。我们提供了一个灵活的Python工具,用于从IFS数据多维数据集进行三维最佳光谱提取(TDOSE)。 TDOSE可在任何三维数据立方体上工作,并将光谱提取基于形态学参考图像模型。默认情况下,这些模型是由多个多元高斯分量生成并组成的,但也可以使用独立的建模工具进行构建,并作为TDOSE的输入提供。在IFS数据立方体的每个波长层中,TDOSE都会同时优化形态模型中的所有光源,以最大程度地缩小比例模型分量和IFS数据之间的差异。通量优化产生包含缩放的三维源模型的单个数据立方体。这允许在IFS数据立方体的空间和光谱维度上有效地消除通量,并提取相应的一维光谱。 TDOSE隐式要求有关二维光分布的假设。我们描述了如何利用TDOSE的灵活性来减轻和校正输入分布的偏差。此外,我们提供一个示例,说明当磁通量污染成为问题时,如何将TDOSE生成的三维源模型用于改善物理参数(如速度,金属度或恒星形成率)的二维图。通过从MUSE-Wide调查中提取约150个[OII]发射器的TDOSE光谱,我们发现,使用多分量模型而不是单分量模型时,线通量的中值增加为5%。但是,在个别情况下,回收线路发射的增加可能高达50%。将基于TDOSE模型的MUSE宽[OII]发射器的提取与孔径光谱进行比较,TDOSE光谱的中值通量(S / N)增加了9%(14%)。因此,TDOSE光谱优化了S / N,同时仍然能够恢复总发射通量。

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