Predicting the spectrum of UGC 2885, Rubin’s Galaxy with machine learning. (arXiv:2105.03377v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Holwerda_B/0/1/0/all/0/1">Benne W. Holwerda</a> (University of Louisville), <a href="http://arxiv.org/find/astro-ph/1/au:+Wu_J/0/1/0/all/0/1">John F. Wu</a> (STSCI, JHU), <a href="http://arxiv.org/find/astro-ph/1/au:+Keel_W/0/1/0/all/0/1">William C. Keel</a> (University of Alabama), <a href="http://arxiv.org/find/astro-ph/1/au:+Young_J/0/1/0/all/0/1">Jason Young</a> (Mount Holyoke College), <a href="http://arxiv.org/find/astro-ph/1/au:+Mullins_R/0/1/0/all/0/1">Ren Mullins</a> (University of Louisville), <a href="http://arxiv.org/find/astro-ph/1/au:+Hinz_J/0/1/0/all/0/1">Joannah Hinz</a> (Steward Observatory, MMT Observatory), <a href="http://arxiv.org/find/astro-ph/1/au:+Ford_K/0/1/0/all/0/1">K.E. Saavik Ford</a> (CUNY, AMNH, Flatiron), <a href="http://arxiv.org/find/astro-ph/1/au:+Barmby_P/0/1/0/all/0/1">Pauline Barmby</a> (University of Western Ontario), <a href="http://arxiv.org/find/astro-ph/1/au:+Chandar_R/0/1/0/all/0/1">Rupali Chandar</a> (University of Toledo), <a href="http://arxiv.org/find/astro-ph/1/au:+Bailin_J/0/1/0/all/0/1">Jeremy Bailin</a> (University of Alabama), <a href="http://arxiv.org/find/astro-ph/1/au:+Peek_J/0/1/0/all/0/1">Josh Peek</a> (STSCI/JHU), <a href="http://arxiv.org/find/astro-ph/1/au:+Pickering_T/0/1/0/all/0/1">Tim Pickering</a> (Steward Observatory, MMT Observatory), <a href="http://arxiv.org/find/astro-ph/1/au:+Boker_T/0/1/0/all/0/1">Torsten Böker</a> (ESA/STSCI)
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.
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.
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