http://arxiv.org/abs/1807.05922
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 datacubes. However, a great deal of information available within these datacubes 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 datacube, rather than relying on ancillary imaging. We show that we are able to obtain object spectra comparable or superior to manual extractions. Further, implementing our custom designed algorithms also improves the signal-to-noise of the extracted spectra and successfully deblends 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 into the Python based spectral extraction software, AutoSpec which is available via GitHub at: https://github.com/a-griffiths/AutoSpec
A. Griffiths and C. Conselice
Tue, 17 Jul 18
54/79
Comments: Submitted to ApJ, 8 pages, 4 figures