StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization

TL;DR

StelNet is a hierarchical deep neural network that rapidly and accurately infers stellar mass and age from observational data, effectively handling degeneracies and including pre-main sequence phases.

Contribution

The paper introduces StelNet, a novel hierarchical neural network model that improves the speed and accuracy of stellar parameter inference from evolutionary tracks, including uncertainty quantification.

Findings

StelNet achieves fast predictions of stellar parameters with high accuracy.

The hierarchical model effectively resolves degeneracies in evolutionary tracks.

Uncertainty estimates are reliably quantified through bootstrapping.

Abstract

Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of stellar age and mass enables translating observables into physical stellar parameters by fitting the observed data to synthetic isochrones. However, the complexity of overlapping evolutionary tracks often makes this task numerically challenging, and with a precision that can be highly variable, depending on the area of the parameter space the observation lies in. This work presents StelNet, a Deep Neural Network trained on stellar evolutionary tracks that quickly and accurately predicts mass and age from absolute luminosity and effective temperature for stars with close to solar metallicity. The underlying model makes no assumption on the evolutionary stage and includes the pre-main sequence phase. We use bootstrapping and train many models to quantify the uncertainty of the model. To break the model's intrinsic degeneracy resulting from overlapping evolutionary paths, we also built a hierarchical model that retrieves realistic posterior probability distributions of the stellar mass and age. We further test and train StelNet using a sample of stars with well-determined masses and ages from the literature.