AutoSpec: Fast Automated Spectral Extraction Software for IFU Datacubes
  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. 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 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 into 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
