Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: a catalogue of ∼500 000 H i gas fraction estimates based on artificial neural networks

The application of artificial neural networks (ANNs) for the estimation of H i gas mass fraction (M|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$|⁠) 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|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$| 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|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$|⁠. 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|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$| estimation. In contrast to previous works, our M|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$| estimation has no systematic trend with galactic parameters such as M⋆, g − r and star formation rate. We present a catalogue of M|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$| 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|$_{\rm H\,\small {I}}/{{\it M}_{\ast }}$| estimation of 0.22 dex.

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