Predicting the Spectrum of UGC 2885, Rubin’s Galaxy with Machine Learning

Abstract

Wu & Peek predict SDSS-quality spectra based on Pan-STARRS broadband grizy images using machine learning (ML). In this article, we test their prediction for a unique object, UGC 2885 ( Rubin\u27s galaxy ), the largest and most massive, isolated disk galaxy in the local universe (D \u3c 100 Mpc). After obtaining the ML predicted spectrum, we compare it to all existing spectroscopic information that is comparable to an SDSS spectrum of the central region: two archival spectra, one extracted from the VIRUS-P observations of this galaxy, and a new, targeted MMT/Binospec observation. Agreement is qualitatively good, though the ML prediction prefers line ratios slightly more toward those of an active galactic nucleus (AGN), compared to archival and VIRUS-P observed values. The MMT/Binospec nuclear spectrum unequivocally shows strong emission lines except Hβ, the ratios of which are consistent with AGN activity. The ML approach to galaxy spectra may be a viable way to identify AGN supplementing NIR colors. How such a massive disk galaxy (M* = 1011 M⊙), which uncharacteristically shows no sign of interaction or mergers, manages to fuel its central AGN remains to be investigated