Periodic Reporting for period 3 - CartographY (Mapping Stellar Helium)
Berichtszeitraum: 2022-04-01 bis 2023-09-30
In most real world cases progress has been made by tying initial He to initial metallicity but these relations are at best poorly calibrated, or at worst completely inappropriate. This systematic failure limits our understanding of our universe when we try to understand stars, the demographics of exoplanets, and the structure and evolution of our own Milky Way.
We have addressed a number of challenges of applying BHM's to the problem of stellar evolution. By far the most challenging aspect of the problem, is that using, models of stellar evolution is slow - very slow. We have used machine learning to speed up this step by producing a neural network emulator of stellar evolution. This step has reduced out computational time scale by 6 orders of magnitude and given us access to mathematical properties of our models that would otherwise be impossible to calculate.
By combining our neural network emulator with state-of-the-art probabilistic programming languages we have been able to simultaneously model the population and the individual properties of 80 stars. And this method we have scales well. Tests of artificial data suggest we could now deploy our method to over tern thousand stars with a run time of less than a single day.