كيف نسمع عمر النجم؟ سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2601.02926

Accurately measuring stellar ages and internal structures is challenging, but the inclusion of asteroseismic observables can substantially improve precision. However, the curse of dimensionality means this comes at a high computational cost when using standard interpolation methods across grids of stellar models. Furthermore, without a rigorous treatment of random uncertainties in grid-based modelling, it is not possible to address systematic errors in stellar models. We present PITCHFORK -- a multilayer perceptron neural network with a branching architecture capable of rapid emulation of both classical stellar observables and individual asteroseismic oscillation modes of solar-like oscillators. PITCHFORK can predict the classical observables $$T_{\text{eff}}$$, $$L$$, and $$\left[\mathrm{Fe}/\mathrm{H}\right]$$ with precisions of $$5.88\,\text{K}$$, $$0.014\,\text{L}_{\odot}$$, and $$0.001\,\text{dex}$$, respectively, and can predict 35 individual radial mode frequencies with a uniform precision of $$0.02$$ per cent. PITCHFORK is coupled to a vectorised Bayesian inference pipeline to return well-sampled and fully marginalised posterior distributions. We validate our rigorous treatment of the random uncertainties -- including the asteroseismic surface effect -- in an extensive hare-and-hounds exercise. We also demonstrate our ability to infer the stellar properties of benchmark stars -- namely, the Sun and the binary stars 16 Cygni A and B. This work demonstrates a computationally scalable and statistically robust framework for stellar parameter inference of solar-like oscillators using individual asteroseismic mode frequencies. This provides a foundation for the treatment of systematics in preparation for the imminent abundance of asteroseismic data from future missions.