  Multiple explosion mechanisms have been proposed to explain type Ia
supernovae (SNe Ia). Empirical modelling tools have also been developed that
allow for fast, customised modelling of individual SNe and direct comparisons
between observations and explosion model predictions. Such tools have provided
useful insights, but the subjective nature with which empirical modelling is
performed makes it difficult to obtain robust constraints on the explosion
physics or expand studies to large populations of objects. Machine learning
accelerated tools have therefore begun to gain traction. In this paper, we
present riddler, a framework for automated fitting of SNe Ia spectral sequences
up to shortly after maximum light. We train a series of neural networks on
realistic ejecta profiles predicted by the W7 and N100 explosion models to
emulate full radiative transfer simulations and apply nested sampling to
determine the best-fitting model parameters for multiple spectra of a given SN
simultaneously. We show that riddler is able to accurately recover the
parameters of input spectra and use it to fit observations of two well-studied
SNe Ia. We also investigate the impact of different weighting schemes when
performing quantitative spectral fitting and show that best-fitting models and
parameters are highly dependent on the assumed weighting schemes and priors. As
spectroscopic samples of SNe Ia continue to grow, automated spectral fitting
tools such as riddler will become increasingly important to maximise the
physical constraints that can be gained in a quantitative and consistent
manner.
