Artificial Intelligence Assisted Inversion (AIAI): Quantifying the Spectral Features of 56Ni of Type Ia Supernovae

Abstract

Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses, we train a set of deep neural networks based on the 1D radiative transfer code TARDIS to simulate the optical spectra of Type Ia supernovae (SNe Ia) between 10 and 40 days after the explosion. The neural networks are applied to derive the mass of ^56 Ni in velocity ranges above the photosphere for a sample of 124 well-observed SNe Ia in the TARDIS model context. A subset of the SNe have multi-epoch observations for which the decay of the radioactive ^56 Ni can be used to test the AIAI quantitatively. The ^56 Ni mass derived from AIAI using the observed spectra as inputs for this subset agrees with the radioactive decay rate of ^56 Ni. AIAI reveals that a spectral signature near 3890 Å is related to the Ni ii 4067Å line, and the ^56 Ni mass deduced from AIAI is found to be correlated with the light-curve shapes of SNe Ia, with SNe Ia with broader light curves showing larger ^56 Ni mass in the envelope above the photosphere. AIAI enables spectral data of SNe to be quantitatively analyzed under theoretical frameworks based on well-defined physical assumptions.

Keywords

• Type Ia supernovae
• Convolutional neural networks