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.
- Publication:
-
The Astrophysical Journal
- Pub Date:
- June 2021
- DOI:
- 10.3847/1538-4357/abffcc
- arXiv:
- arXiv:2105.03377
- Bibcode:
- 2021ApJ...914..142H
- Keywords:
-
- Giant galaxies;
- Galaxy nuclei;
- Disk galaxies;
- 652;
- 609;
- 391;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- 9 pages, 5 figures, submitted to ApJL