AutoSpec: Fast Automated Spectral Extraction Software for IFU Data Cubes
AutoSpec: Fast Automated Spectral Extraction Software for IFU Data Cubes
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DOI：
10.3847/1538-4357/aaee87
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发表时间：
2018-07
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影响因子：
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通讯作者：
A. Griffiths;C. Conselice
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文献类型：
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作者：
A. Griffiths;C. Conselice
随着积分场单元 (IFU) 光谱学的日益普及，人们对空白场和星系团等多个物体系统进行了无数的观测。因此，从大型三维数据立方体中提取一维物体光谱所花费的时间越来越多。然而，这些数据立方体中的大量可用信息被忽视，而有利于基于光度测量的空间信息。在这里，我们提出了一种新颖而简单的最佳源识别方法，利用 IFU 数据立方体中可用的丰富信息，而不是依赖辅助成像。通过这些技术的应用，我们表明我们能够获得与深度光度加权提取相当的物体光谱，而无需辅助成像。此外，实施我们定制设计的算法可以提高提取光谱的信噪比，并成功地去除附近污染物的来源。这将是未来 IFU 空白和深场观测的关键工具，特别是在需要自动化的大面积区域。我们在基于 Python 的光谱提取软件 AutoSpec 中实现了这些技术，该软件可通过 GitHub（https://github.com/a-griffiths/AutoSpec）和 Zenodo（https://doi.org/10.5281/zenodo.1305848）获得。
With the ever-growing popularity of integral field unit (IFU) spectroscopy, countless observations are being performed over multiple object systems such as blank fields and galaxy clusters. With this, an increasing amount of time is being spent extracting one-dimensional object spectra from large three-dimensional data cubes. However, a great deal of information available within these data cubes is overlooked in favor of photometrically based spatial information. Here we present a novel yet simple approach of optimal source identification utilizing the wealth of information available within an IFU data cube, rather than relying on ancillary imaging. Through the application of these techniques, we show that we are able to obtain object spectra comparable to deep photometry-weighted extractions without the need for ancillary imaging. Further, implementing our custom-designed algorithms can improve the signal-to-noise ratio of extracted spectra and successfully deblend sources from nearby contaminants. This will be a critical tool for future IFU observations of blank and deep fields, especially over large areas where automation is necessary. We implement these techniques in the Python-based spectral extraction software, AutoSpec, which is available via GitHub at https://github.com/a-griffiths/AutoSpec and Zenodo at https://doi.org/10.5281/zenodo.1305848.