StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization

dc.contributor.authorGarraffo, Cecilia
dc.contributor.authorProtopapas, Pavlos
dc.contributor.authorDrake, Jeremy J.
dc.contributor.authorBecker, Ignacio
dc.contributor.authorCargile, Phillip
dc.date.accessioned2025-01-20T22:08:13Z
dc.date.available2025-01-20T22:08:13Z
dc.date.issued2021
dc.description.abstractCharacterizing 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.
dc.description.funderNASA
dc.fuente.origenWOS
dc.identifier.doi10.3847/1538-3881/ac0ef0
dc.identifier.eissn1538-3881
dc.identifier.issn0004-6256
dc.identifier.urihttps://doi.org/10.3847/1538-3881/ac0ef0
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/94282
dc.identifier.wosidWOS:000697836800001
dc.issue.numero4
dc.language.isoen
dc.revistaAstronomical journal
dc.rightsacceso restringido
dc.titleStelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization
dc.typeartículo
dc.volumen162
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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