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 [Formula: see text]CDM 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.
Modelling the spectral energy distribution of galaxies: introducing the artificial neural network
