Researchers from the University of Sydney, led by Owen J. Scutt, have developed a novel approach to improve the precision of stellar age and internal structure measurements, which could have significant implications for the energy sector, particularly in nuclear fusion research. The team, which includes Guy R. Davies, Amalie Stokholm, Alexander J. Lyttle, Martin B. Nielsen, Emily Hatt, Tanda Li, Mikkel N. Lund, and Timothy R. Bedding, has published their findings in the journal Nature Astronomy.
The study focuses on asteroseismology, the study of stellar oscillations, which can provide valuable insights into the internal workings of stars. Accurate measurements of stellar ages and internal structures are challenging due to the high computational cost and systematic errors in traditional modeling methods. To address these issues, the researchers developed PITCHFORK, a neural network with a branching architecture capable of rapidly emulating both classical stellar observables and individual asteroseismic oscillation modes of solar-like oscillators.
PITCHFORK can predict classical observables such as effective temperature (Teff), luminosity (L), and iron abundance ([Fe/H]) with high precision. It can also predict 35 individual radial mode frequencies with a uniform precision of 0.02 percent. The neural network is coupled to a vectorized Bayesian inference pipeline, which returns well-sampled and fully marginalized posterior distributions. This rigorous treatment of random uncertainties, including the asteroseismic surface effect, was validated through an extensive hare-and-hounds exercise and demonstrated on benchmark stars, including the Sun and the binary stars 16 Cygni A and B.
The practical applications of this research for the energy sector, particularly in nuclear fusion, are significant. Accurate measurements of stellar ages and internal structures can provide valuable insights into the processes that power stars, which in turn can inform the development of fusion energy technologies. By improving the precision of stellar parameter inference, PITCHFORK can help researchers better understand the conditions necessary for nuclear fusion, ultimately contributing to the development of cleaner, more sustainable energy sources. The researchers’ work provides a computationally scalable and statistically robust framework for stellar parameter inference of solar-like oscillators, paving the way for the treatment of systematics in preparation for the imminent abundance of asteroseismic data from future missions.
This article is based on research available at arXiv.