Non-parametric cell-based photometric proxies for galaxy morphology: methodology and application to the morphologically defined star formation-stellar mass relation of spiral galaxies in the local universe

We present a non-parametric cell-based method of selecting highly pure and largely complete samples of spiral galaxies using photometric and structural parameters as provided by standard photometric pipelines and simple shape fitting algorithms. The performance of the method is quantified for different parameter combinations, using purely human-based classifications as a benchmark. The discretization of the parameter space allows a markedly superior selection than commonly used proxies relying on a fixed curve or surface of separation. Moreover, we find structural parameters derived using passbands longwards of the g band and linked to older stellar populations, especially the stellar mass surface density μ_* and the r-band effective radius r_e, to perform at least equally well as parameters more traditionally linked to the identification of spirals by means of their young stellar populations, e.g. UV/optical colours. In particular, the distinct bimodality in the parameter μ_*, consistent with expectations of different evolutionary paths for spirals and ellipticals, represents an often overlooked yet powerful parameter in differentiating between spiral and non-spiral/elliptical galaxies. We use the cell-based method for the optical parameter set including r_e in combination with the Sérsic index n and the i-band magnitude to investigate the intrinsic specific star formation rate-stellar mass relation (ψ_*-M_*) for a morphologically defined volume-limited sample of local Universe spiral galaxies. The relation is found to be well described by ψ_* α M_*^-0.5over the range of 10^9.5 ≤ M_* ≤ 10¹¹M_⊙ with a mean interquartile range of 0.4 dex. This is somewhat steeper than previous determinations based on colour-selected samples of starforming galaxies, primarily due to the inclusion in the sample of red quiescent discs.