  Type Ia supernovae 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 sub-types of Type Ia
supernovae have been identified in recent decades. Due to the massive
computational load required, inference of the internal structure of Type Ia
supernovae 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 supernovae from optical spectra using the
radiative transfer code TARDIS accelerated by the probabilistic DALEK
deep-learning emulator. We apply a parametric model of potential ejecta
structures to comparatively investigate abundance distributions and internal
ionization fractions of intermediate-mass elements between normal and
1991T-like Type Ia supernovae. Our inference shows that 1991T-like Type Ia
supernovae are under-abundant in the typical intermediate mass elements that
heavily contribute to the spectral line formation seen in normal Type Ia
supernovae at early times. Additionally, we find that the intermediate-mass
elements present in 1991T-like Type Ia supernovae 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 supernovae appears to be
continuous observationally and that the observed differences come out of a
combination of both abundance and ionization fractions in these supernovae
populations.

High Energy Astrophysical Phenomena