Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine
  learning
  Wu & Peek (2020) predict SDSS-quality spectra based on Pan-STARRS broad-band
\textit{grizy} images using machine learning (ML). In this letter, 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
towards 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 H$\beta$, 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 ($M^* = 10^{11}$
M$_\odot$), which uncharacteristically shows no sign of interaction or mergers,
manages to fuel its central AGN remains to be investigated.
