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's galaxy"), the largest and most massive, isolated disk galaxy in the local universe (D < 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.