The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate parameter. After training and testing the neural network, the SED sequence could give both the spectrum with wavelength range from 3000 Åto 8000 Åand the light curve with phase from 20 days before to 50 days after the maximum luminosity for the supernovae with different colors and decline rates. Therefore, we call this the Artificial Neural Network Spectral Light Curve Template (ANNSLCT) model. We retrain the Joint Light-curve Analysis (JLA) supernova sample by using the ANNSLCT model and obtain the parameters for each supernova to make a constraint on the cosmological ΛCDM model. We find that the best fitting values of these parameters are very close to those from the JLA sample trained with the Spectral Adaptive Lightcurve Template 2 (SALT2) model. It is expectable that the ANNSLCT model has potential to analyze more SN Ia multi-color light curves measured in future observation projects.

中文翻译：

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Ia型超新星的人工神经网络光谱光曲线模板及其宇宙学约束

Ia 型超新星 (SN Ia) 的光谱能量分布 (SED) 序列由人工神经网络建模。SN Ia 光度被表征为相位、波长、颜色参数和衰减率参数的函数。经过对神经网络的训练和测试，SED序列可以给出不同颜色和衰减率的超新星最大光度前20天到后50天的光谱，以及波长范围从3000埃到8000埃的光度曲线。因此，我们将其称为人工神经网络光谱光曲线模板 (ANNSLCT) 模型。我们使用 ANNSLCT 模型重新训练联合光曲线分析 (JLA) 超新星样本，并获得每个超新星的参数以对宇宙学进行约束Λ清洁发展机制模型。我们发现这些参数的最佳拟合值非常接近使用光谱自适应光曲线模板 2 (SALT2) 模型训练的 JLA 样本。可以预见的是，ANNSLCT 模型具有分析未来观测项目中测量的更多 SN Ia 多色光变曲线的潜力。