Many stars end as white dwarfs, compact stars with the mass of the Sun but the size of Earth. Some white dwarfs explode as supernovae, releasing heavy elements like calcium and iron into the Universe, which are crucial for life. However, the exact mechanisms behind these explosions remain unknown.

New research aims to use machine learning, a type of AI, to speed up the analysis of supernovae. This technique will compare explosion models to real-life observations, which currently require extensive computational resources and time.

Dr. Mark Magee, lead author from the University of Warwick's Department of Physics, explained, "When investigating supernovae, we analyse their spectra. Spectra show the intensity of light over different wavelengths, which is impacted by the elements created in the supernova. Each element interacts with light at unique wavelengths and therefore leaves a unique signature on the spectra."

He added, "Analysing these signatures can help to identify what elements are created in a supernova and provide further details on how the supernovae exploded."

Dr. Magee continued, "From this data, we prepare models, which are compared to real supernovae to establish what type of supernova it is and exactly how it exploded. Typically, one model might take 10 - 90 minutes to generate and we want to compare hundreds or thousands of models to fully understand the supernova. This isn't really feasible in many cases."

"Our new research will move away from this lengthy process. We will train machine learning algorithms on what different types of explosions look like and use these to generate models much more quickly. In a similar way to how we can use AI to generate new artwork or text, now we'll be able to generate simulations of supernovae. This means we'll be able to generate thousands of models in less than a second, which will be a huge boost to supernova research."

The use of AI not only accelerates supernova analysis but also enhances accuracy, helping to identify models that best match real explosions and their properties.

Dr. Magee stated, "Exploring the elements released by supernovae is a crucial step in determining the type of explosion that occurred, as certain types of explosions produce more of some elements than others. We can then relate the properties of the explosion back to the properties of the supernova host galaxies and establish a direct link between how the explosion happened and the type of white dwarf that exploded."

Future research will expand to include a broader variety of explosions and supernovae, directly linking explosion and host galaxy properties. Advances in machine learning are making such research possible.

Dr. Thomas Killestein from the University of Turku, also involved in the research, commented, "With modern surveys, we finally have datasets of the size and quality to tackle some of the key remaining questions in supernova science: how exactly they explode. Machine learning approaches like this enable studies of larger numbers of supernovae, in greater detail, and with more consistency than previous approaches."

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

Related Links
University of Warwick
Stellar Chemistry, The Universe And All Within It