English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Characterizing the fundamental parameters of stars from observations iscrucial for studying the stars themselves, their planets, and the galaxy as awhole. Stellar evolution theory predicting the properties of stars as afunction of stellar age and mass enables translating observables into physicalstellar parameters by fitting the observed data to synthetic isochrones.However, the complexity of overlapping evolutionary tracks often makes thistask numerically challenging, and with a precision that can be highly variable,depending on the area of the parameter space the observation lies in. This workpresents StelNet, a Deep Neural Network trained on stellar evolutionary tracksthat quickly and accurately predicts mass and age from absolute luminosity andeffective temperature for stars with close to solar metallicity. The underlyingmodel makes no assumption on the evolutionary stage and includes the pre-mainsequence phase. We use bootstrapping and train many models to quantify theuncertainty of the model. To break the model's intrinsic degeneracy resultingfrom overlapping evolutionary paths, we also built a hierarchical model thatretrieves realistic posterior probability distributions of the stellar mass andage. We further test and train StelNet using a sample of stars withwell-determined masses and ages from the literature.

StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization