2018/07/31 by Alex Griffiths, Christopher J. Conselice · 4 citations
Environmental Science · Physics and Astronomy · #Astronomy and Astrophysical Research #Blank #Computer science #Data cube #Data extraction #Data mining #Galaxies: Formation, Evolution, Phenomena #Python (programming language) #Remote Sensing in Agriculture #Software #astro-ph.GA #astro-ph.IM
paper · pdf · doi:10.3847/1538-4357/aaee87
published in The Astrophysical Journal 869(1), 68 (IOP Publishing) · Accepted by ApJ, 10 pages, 5 figures,
openalex created_date 2018/08/03 · openalex publication_date 2018/12/10 · arxiv created 2018/12/17 · arxiv updated 2018/12/18 · openalex updated_date 2026/08/06
Abstract 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 10.5281/zenodo.1305848 .