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Abstract

Type Ia supernovae (SNe) remain poorly understood despite decades of investigation. Massive computationally intensive hydrodynamic simulations have been developed and run to model an ever-growing number of proposed progenitor channels. Further complicating the matter, a large number of subtypes of Type Ia SNe have been identified in recent decades. Due to the massive computational load required, inference of the internal structure of Type Ia SNe ejecta directly from observations using simulations has previously been computationally intractable. However, deep-learning emulators for radiation transport simulations have alleviated such barriers. We perform abundance tomography on 40 Type Ia SNe from optical spectra using the radiative transfer code TARDIS accelerated by the probabilistic DALEK deep-learning emulator. We apply a parametric model of potential outer ejecta structures to comparatively investigate abundance distributions and internal ionization fractions of intermediate-mass elements (IMEs) between normal and 1991T-like Type Ia SNe in the early phases. Our inference shows that the outer ejecta of 1991T-like Type Ia SNe is underabundant in the typical intermediate mass elements that heavily contribute to the spectral line formation seen in normal Type Ia SNe at early times. Additionally, we find that the IMEs present in 1991T-like Type Ia SNe are highly ionized compared to those in the normal Type Ia population. Finally, we conclude that the transition between normal and 1991T-like Type Ia SNe appears to be continuous observationally and that the observed differences come out of a combination of both abundance and ionization fractions in these SNe populations.