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Stellar masses, sizes, and radial profiles for 465 nearby early-type galaxies: an extension to the Spitzer Survey of Stellar Structure in Galaxies (S$^{4}$G) (2201.08381v1)

Published 20 Jan 2022 in astro-ph.GA

Abstract: The Spitzer Survey of Stellar Structure in Galaxies (S${4}$G) is a detailed study of over 2300 nearby galaxies in the near-infrared (NIR), which has been critical to our understanding of the detailed structures of nearby galaxies. Because the sample galaxies were selected only using radio-derived velocities, however, the survey favored late-type disk galaxies over lenticulars and ellipticals. A follow-up Spitzer survey was conducted to rectify this bias, adding 465 early-type galaxies (ETGs) to the original sample, to be analyzed in a manner consistent with the initial survey. We present the data release of this ETG extension, up to the third data processing pipeline (P3): surface photometry. We produce curves of growth and radial surface brightness profiles (with and without inclination corrections) using reduced and masked Spitzer IRAC 3.6$\mu$m and 4.5$\mu$m images produced through Pipelines 1 and 2, respectively. From these profiles, we derive the following integrated quantities: total magnitudes, stellar masses, concentration parameters, and galaxy size metrics. We showcase NIR scaling relations for ETGs among these quantities. We examine general trends across the whole S${4}$G and ETG extension among our derived parameters, highlighting differences between ETGs and late-type galaxies (LTGs). ETGs are, on average, more massive and more concentrated than LTGs, and also show subtle distinctions among ETG morphological sub-types. We also derive the following scaling relations and compare with previous results in visible light: mass--size (both half-light and isophotal), mass--concentration, mass--surface brightness (central, effective, and within 1 kpc), and mass--color. We find good agreement with previous works, though some relations (e.g., mass--central surface brightness) will require more careful multi-component decompositions to be fully understood.

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