论文标题
IA型超新星及其宇宙学限制的人工神经网络光谱曲线模板
Artificial Neural Network Spectral Light Curve Template for Type Ia Supernovae and its Cosmological Constraints
论文作者
论文摘要
IA型超新星（SN IA）的光谱能分布（SED）序列由人工神经网络建模。 SN IA光度的特征是相位，波长，颜色参数和下降速率参数的函数。在训练和测试神经网络之后，SED序列可以使波长范围为3000Å至8000Å〜，以及具有不同颜色和下降速率的超级新星的最大发光度后20天到50天的光曲线。因此，我们将其称为人造神经网络光谱光曲线模板（ANNSLCT）模型。我们通过使用ANNSLCT模型来重新训练关节曲线分析（JLA）超新星样品，并获得每个超新星的参数，以限制宇宙$λ$ CDM模型。我们发现，这些参数的最佳拟合值几乎与用光谱自适应灯曲面模板2（salt2）模型训练的JLA样品中的拟合值几乎相同。因此，我们认为ANNSLCT模型可用于分析当前和将来的观察项目中测得的大量SN IA多色光曲线。
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 3000Å~to 8000Å~ and 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 $Λ$CDM model. We find that the best fitting values of these parameters are almost the same as those from the JLA sample trained with the Spectral Adaptive Lightcurve Template 2 (SALT2) model. So we believe that the ANNSLCT model could be used to analyze a large number of SN Ia multi-color light curves measured in the current and future observational projects.
