  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.
