Determine the Masses and Ages of Red Giant Branch Stars from Low-resolution LAMOST Spectra Using DenseNet

TL;DR

This paper introduces a deep learning model based on DenseNet to accurately estimate the ages and masses of red giant branch stars from low-resolution spectra, leveraging asteroseismic data for training.

Contribution

The study presents a novel deep learning approach that directly predicts RGB star ages from spectra, outperforming existing methods and applicable to large survey datasets.

Findings

Achieves 24.3% age estimation accuracy from low-resolution spectra.

Successfully applied to over 500,000 LAMOST RGB stars.

Performs well on open cluster stars, validating model effectiveness.

Abstract

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.