تقرير
Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine learning
| العنوان: | Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine learning |
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| المؤلفون: | Holwerda, Benne W., Wu, John F., Keel, William C., Young, Jason, Mullins, Ren, Hinz, Joannah, Ford, K. E. Saavik, Barmby, Pauline, Chandar, Rupali, Bailin, Jeremy, Peek, Josh, Pickering, Tim, Böker, Torsten |
| سنة النشر: | 2021 |
| المجموعة: | Astrophysics |
| مصطلحات موضوعية: | Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics |
| الوصف: | 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. Comment: 9 pages, 5 figures, submitted to ApJL |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2105.03377 |
| رقم الأكسشن: | edsarx.2105.03377 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |