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arXiv 2102.06230astro-ph.GAastro-ph.IM
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积分场单元光谱观测的机器学习方法:II。区域HII

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios

Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet, Myriam Prasow-Emond, Julie Hlavacek-Larrondo, Natalia Vale Asari, Kathryn Grasha, Laurence Perreault-Levasseur

摘要:

在该系列的第一篇论文中(Rhea et al. 2020),我们证明了神经网络可以鲁棒有效地估计SITELLE在加拿大-法国-夏威夷望远镜上拍摄的光学发射线光谱的运动学参数。本文通过开发一种人工神经网络来估计SITELLE的SN 1,SN 2和SN 3滤波器中存在的强发射线的线比率,从而扩展了这一概念。我们构建了一组50,000合成光谱使用线比从墨西哥百万模型数据库复制Hii地区。测试集上的网络的残差分析揭示了网络的能力,应用严格的约束线比率。我们通过构建激活图,检查[N ii]双峰固定比率,并应用标准k倍互相关来验证网络的功效。此外,我们将该网络应用于M33的SITELLE观测;该算法的估计值和使用标准拟合方法计算的值之间的残差显示出普遍的一致性。此外,神经网络将计算成本降低了两个数量级。虽然标准拟合例程根据光谱特征的信噪比一直做得很好,但神经网络也可以在训练集的受控环境内的低信噪比范围内以及在源光谱特性受到模型良好约束时的观测数据上进行预测。这些结果增强了机器学习在光谱分析中的作用。

英文摘要:

In the first paper of this series (Rhea et al. 2020), we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spectra taken by SITELLE at the Canada-France-Hawaii Telescope. This paper expands upon this notion by developing an artificial neural network to estimate the line ratios of strong emission-lines present in the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000 synthetic spectra using line ratios taken from the Mexican Million Model database replicating Hii regions. Residual analysis of the network on the test set reveals the network's ability to apply tight constraints to the line ratios. We verified the network's efficacy by constructing an activation map, checking the [N ii] doublet fixed ratio, and applying a standard k-fold cross-correlation. Additionally, we apply the network to SITELLE observation of M33; the residuals between the algorithm's estimates and values calculated using standard fitting methods show general agreement. Moreover, the neural network reduces the computational costs by two orders of magnitude. Although standard fitting routines do consistently well depending on the signal-to-noise ratio of the spectral features, the neural network can also excel at predictions in the low signal-to-noise regime within the controlled environment of the training set as well as on observed data when the source spectral properties are well constrained by models. These results reinforce the power of machine learning in spectral analysis.

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。

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