  We propose a new model to determine the ages and masses of red giant branch
(RGB) stars from the low-resolution large sky area multi-object fiber
spectroscopic telescope (LAMOST) spectra. The ages of RGB stars are difficult
to determine using classical isochrone fitting techniques in the
Hertzsprung-Russell diagram, because isochrones of RGB stars are tightly
crowned. With the help of the asteroseismic method, we can determine the masses
and ages of RGB stars accurately. Using the ages derived from the asteroseismic
method, we train a deep learning model based on DenseNet to calculate the ages
of RGB stars directly from their spectra. We then apply this model to determine
the ages of 512 272 RGB stars from LAMOST DR7 spectra (see
http://dr7.lamost.org/). The results show that our model can estimate the ages
of RGB stars from low-resolution spectra with an accuracy of 24.3%. The results
on the open clusters M 67, Berkeley 32, and NGC 2420 show that our model
performs well in estimating the ages of RGB stars. Through comparison, we find
that our method performs better than other methods in determining the ages of
RGB stars. The proposed method can be used in the stellar parameter pipeline of
upcoming large surveys such as 4MOST, WEAVES, and MOONS.
