J-PAS：使用人工神经网络测量发射谱线
J-PAS: Measuring emission lines with artificial neural networks
摘要：
在本文中，我们提出了一种新的方法来检测和测量发射线的J-PAS高达$z = 0.35$。J-PAS将在未来几年内使用56个测光波段观测北半球天空的$8000$~度$^2$。如此大量的数据的发布为我们带来了采用机器学习方法的机会，以克服与光度数据相关的困难。我们使用人工神经网络（ANN）训练和测试与合成J-PAS光度从CALIFA，MANGA，和SDSS光谱。我们执行两项任务：首先，我们根据测量的$Hα$、$Hβ$、$[NII]{λ6584}$和$ [OIII]{λ5007}$谱线的等效宽度（EW）值将星系分为两组。然后，我们训练一个ANN来为每个星系分配一个组。我们能够将它们与J-PAS中可测量的光度红移的典型不确定性进行分类。其次，我们利用另一个人工神经网络来确定这些EW的值。随后，我们获得了恢复BPT图的$[NII]/Hα$、$[OIII]/Hβ$和\ion{O}{3} \ion{N}{2}比率。我们研究了两个训练样本的人工神经网络的性能：一个是只由合成的J-PAS光光谱（J-光谱）从MANGA和CALIFA（CALMa集），另一个是由SDSS星系。通过对EWs的测定，我们可以较好地再现恒星形成星系的主要序列。使用CALMa训练集，我们在SDSS测试样本中的$[NII]/Hα$和$[OIII]/Hβ$比率达到0.093和0.081 dex的精度。尽管如此，我们发现低估了这些比率在高值的星系托管活动星系核。我们还展示了用于训练和测试模型的数据集的重要性。人工神经网络是非常有用的，以克服以前预期的限制有关的检测和测量的发射线的调查，如J-PAS。
英文摘要：
Throughout this paper we present a new method to detect and measure emission lines in J-PAS up to $z = 0.35$. J-PAS will observe $8000$~deg$^2$ of the northern sky in the upcoming years with 56 photometric bands. The release of such amount of data brings us the opportunity to employ machine learning methods in order to overcome the difficulties associated with photometric data. We used Artificial Neural Networks (ANNs) trained and tested with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. We carry out two tasks: firstly, we cluster galaxies in two groups according to the values of the equivalent width (EW) of $Hα$, $Hβ$, $[NII]{λ6584}$, and $ [OIII]{λ5007}$ lines measured in the spectra. Then, we train an ANN to assign to each galaxy a group. We are able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Secondly, we utilize another ANN to determine the values of those EWs. Subsequently, we obtain the $[NII]/Hα$, $[OIII]/Hβ$, and \ion{O}{3}\ion{N}{2} ratios recovering the BPT diagram . We study the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We can reproduce properly the main sequence of star forming galaxies from the determination of the EWs. With the CALMa training set we reach a precision of 0.093 and 0.081 dex for the $[NII]/Hα$ and $[OIII]/Hβ$ ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an AGN. We also show the importance of the dataset used for both training and testing the model. ANNs are extremely useful to overcome the limitations previously expected concerning the detection and measurements of the emission lines in surveys like J-PAS.
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