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

TL;DR

This paper introduces a deep learning approach called AIAI to analyze Type Ia supernova spectra, accurately estimating the 56Ni mass and revealing spectral signatures linked to supernova properties.

Contribution

The study develops neural networks trained on radiative transfer models to quantify 56Ni in SNe Ia spectra, providing a new quantitative spectral analysis method.

Findings

AIAI-derived 56Ni masses agree with decay rates.

Identified Ni II spectral signature near 3890 Å.

Correlated 56Ni mass with supernova light-curve width.

Abstract

Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses (Chen et al. 2020), we train a set of deep neural networks based on the one-dimensional radiative transfer code TARDIS (Kerzendorf & Sim 2014) 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 56Ni in velocity ranges well above the photosphere for a sample of 153 well-observed SNe Ia. Many SNe have multi-epoch observations for which the decay of the radioactive 56Ni can be tested quantitatively. The 56Ni mass derived from AIAI using the observed spectra as input for the sample is found to agree with the theoretical 56Ni decay rate. The AIAI reveals a spectral signature near 3890 \AA which can be identified as being produced by multiple Ni II lines between 3950 and 4100 \AA. The mass deduced from AIAI is correlated to the light-curve shapes of SNe Ia, with the SNe Ia with broader light curves showing larger 56Ni mass in the envelope. AIAI enables spectral data of SNe to be quantitatively analyzed under theoretical frameworks based on well-defined physical assumptions.