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arXiv 1401.7384astro-ph.GA
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预测银河系[ NII ] $λ$ 6584和H $α$线光度的基于人工神经网络的定标

Artificial neural network based calibrations for the prediction of galactic [NII] $λ$ 6584 and H$α$ line luminosities

Hossein Teimoorinia, Sara L. Ellison

摘要:

人工神经网络(ANN)是一种成熟的数据预测数学技术,基于输入训练集中的相关性识别和模式识别。本文应用人工神经网络预测星系中H $α$和[NII] $λ$ 6584的发射线光度。这些重要的光谱诊断被用于金属丰度、活动星系核分类和恒星形成率,但对于$z \sim 0.5$以上的星系,这些光谱诊断被转移到红外线,或者可能没有被覆盖在有限波长范围的光谱中。人工神经网络的训练与大样本的发射线星系选择从斯隆数字巡天使用各种组合的发射线和恒星质量。人工神经网络进行测试的星系占主导地位的恒星形成和活动星系核,在这两种情况下的H $α$和[NII] $λ$ 6584线光度可以预测与散射$σ<$ 0.1 dex。我们还表明,人工神经网络的性能不依赖于覆盖分数,质量或金属含量的数据显着。多项式函数的推导,使人工神经网络的预测,以确定H $α$和[NII] $λ$ 6584线光度容易的应用。提出了一种基于谱线等效宽度和颜色的巴耳末衰减量(H $α$ /H $β$)的人工神经网络标定方法。人工神经网络校准的有效性证明了一个独立的数据集(银河质量和组装调查)。我们展示了我们的线光度的气相金属丰度和AGN分类的测定中的应用。与Kewley & Ellison的基于函数的转换相比,ANN技术在需要[NII]和H $α$的金属丰度测量中产生了显着的改进。AGN的分类成功的SDSS星系的86%。

英文摘要:

The artificial neural network (ANN) is a well-established mathematical technique for data prediction, based on the identification of correlations and pattern recognition in input training sets. We present the application of ANNs to predict the emission line luminosities of H$α$ and [NII] $λ$6584 in galaxies. These important spectral diagnostics are used for metallicities, active galactic nuclei (AGN) classification and star formation rates, yet are shifted into the infra-red for galaxies above $z \sim 0.5$, or may not be covered in spectra with limited wavelength coverage. The ANN is trained with a large sample of emission line galaxies selected from the Sloan Digital Sky Survey using various combinations of emission lines and stellar mass. The ANN is tested for galaxies dominated by both star formation and AGN; in both cases the H$α$ and [NII] $λ$6584 line luminosities can be predicted with a scatter $σ<$ 0.1 dex. We also show that the performance of the ANN does not depend significantly on the covering fraction, mass or metallicity of the data. Polynomial functions are derived that allow easy application of the ANN predictions to determine H$α$ and [NII] $λ$6584 line luminosities. An ANN calibration for the Balmer decrement (H$α$/H$β$) based on line equivalent widths and colours is also presented. The effectiveness of the ANN calibration is demonstrated with an independent dataset (the Galaxy Mass and Assembly Survey). We demonstrate the application of our line luminosities to the determination of gas-phase metallicities and AGN classification. The ANN technique yields a significant improvement in the measurement of metallicities that require [NII] and H$α$ when compared with the function based conversions of Kewley & Ellison. The AGN classification is successful for 86 per cent of SDSS galaxies.

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