用机器学习方法预测鲁宾星系UGC 2885的光谱
Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine learning
摘要：
Wu & Peek（2020）使用机器学习（ML）基于Pan-STARRS宽带\textit{grizy}图像预测SDS质量光谱。在这封信中，我们测试了他们对一个独特物体的预测，UGC 2885（“鲁宾星系”），它是本机宇宙（$D<100$ Mpc）中最大、最大、最孤立的盘星系。在获得ML预测光谱后，我们将其与所有现有的光谱信息进行比较，这些光谱信息与中心区域的SDSS光谱相当：两个存档光谱，一个从该星系的VIRUS-P观测中提取，另一个是新的，有针对性的MMT/Binospec观测。协议是定性好，虽然ML预测倾向于线比略多对那些活动星系核（AGN），档案和VIRUS-P观测值相比。MMT/Binospec核谱明确地显示出除了H $β$之外的强发射线，其比率与活动星系核活动一致。星系光谱的ML方法可能是一种可行的方法来识别活动星系核补充近红外颜色。这样一个巨大的盘状星系（$M^* = 10^{11}$ M $_\odot$），一反常态地没有任何相互作用或合并的迹象，如何设法为其中心活动星系核提供燃料还有待研究。
英文摘要：
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$β$, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
