Flow-Based Generative Emulation of Grids of Stellar Evolutionary Models
Marc Hon, Yaguang Li, Joel Ong

TL;DR
This paper introduces a flow-based generative model to emulate stellar evolutionary model grids, enabling efficient parameter inference and revealing insights into stellar populations and parameter uncertainties.
Contribution
The novel application of conditional normalizing flows to emulate complex stellar model grids and perform Bayesian inference on large asteroseismic datasets.
Findings
Flow models accurately emulate stellar evolutionary tracks and isochrones.
Revised stellar parameters show improved agreement with existing models.
Identified overestimation of masses in previous asteroseismic analyses.
Abstract
We present a flow-based generative approach to emulate grids of stellar evolutionary models. By interpreting the input parameters and output properties of these models as multi-dimensional probability distributions, we train conditional normalizing flows to learn and predict the complex relationships between grid inputs and outputs in the form of conditional joint distributions. Leveraging the expressive power and versatility of these flows, we showcase their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters. In addition, we describe a simple Bayesian approach for estimating stellar parameters using these flows and demonstrate its application to asteroseismic datasets of red giants observed by the Kepler mission. By applying this approach to red giants in open clusters NGC 6791 and NGC 6819, we illustrate how large age…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Simulation Techniques and Applications · Reinforcement Learning in Robotics
MethodsNormalizing Flows
