2016/10/07 by Hossen Teimoorinia, Sara L. Ellison, David R. Patton
Computer Science · Physics and Astronomy · #Advanced Vision and Imaging #Artificial intelligence #Artificial neural network #Astronomy and Astrophysical Research #Astrophysics #Bulge #Galaxies: Formation, Evolution, Phenomena #Galaxy #Galaxy formation and evolution #Mass fraction #Physics #Sky #Star formation #Stellar mass #Surface brightness #astro-ph.GA
paper · pdf · doi:10.1093/mnras/stw2606
Accepted for publication in MNRAS. 16 pages, 22 figures, 2 tables
arxiv created 2016/10/07 · openalex publication_date 2016/10/11 · openalex created_date 2016/10/21 · arxiv updated 2016/12/07 · openalex updated_date 2026/08/05
The application of artificial neural networks (ANNs) for the estimation of H i gas mass fraction (M|_\rm H \small I/\it M∗ |) is investigated, based on a sample of 13 674 galaxies in the Sloan Digital Sky Survey (SDSS) with H i detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA). We show that, for an example set of fixed input parameters (g − r colour and i-band surface brightness), a multidimensional quadratic model yields M|_\rm H \small I/\it M∗ | scaling relations with a smaller scatter (0.22 dex) than traditional linear fits (0.32 dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches. A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that g − r colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with M|_\rm H \small I/\it M∗ |. By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set (PR) and the scatter in the training procedure (σfit), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated errors in the M|_\rm H \small I/\it M∗ | estimation. In contrast to previous works, our M|_\rm H \small I/\it M∗ | estimation has no systematic trend with galactic parameters such as M⋆, g − r and star formation rate. We present a catalogue of M|_\rm H \small I/\it M∗ | estimates for more than half a million galaxies in the SDSS, of which ∼150 000 galaxies have a secure selection parameter with average scatter in the M|_\rm H \small I/\it M∗ | estimation of 0.22 dex.