Quantitative modelling of type Ia supernovae spectral time series: Constraining the explosion physics

Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Through forward modelling, synthetic observables of explosion models can be directly compared to observations of SNe Ia to constrain the explosion physics. Due to the computational expense associated with multi-dimensional explosion and radiative transfer simulations however, empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe. 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. 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.