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Type Ia supernovae: Constraining thermonuclear explosion physics with machine learning

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Type Ia supernovae are thermonuclear explosions of white dwarfs in binary systems. They play an important role in many areas of astrophysics, from providing chemical enrichment for galaxies to acting as cosmological distance probes. In spite of this, we still fundamentally do not know how or why some white dwarfs explode as thermonuclear supernovae. Multiple explosion mechanisms have been proposed, but the computational expense associated with developing realistic explosion simulations and the difficulty in observing key diagnostic signatures mean that providing robust constraints on the explosion physics is challenging. In this talk, I will provide a general overview of thermonuclear explosion physics and discuss the main explosion scenarios suggested in the literature. I will present my recent work focused on using machine learning to automatically fit spectral sequences of type Ia supernovae in a much more quantitative and efficient way than existing methods. With automated fitting we can test different explosion scenarios against observations and statistically determine which scenario provides the best overall agreement. As spectroscopic samples of supernovae continue to grow, automated fitting tools will become increasingly important to maximise the physical constraints that can be gained in a quantitative and consistent manner.

This talk is part of the Institute of Astronomy Seminars series.

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