  White dwarfs represent the end stage for 97% of stars, making precise
parameter measurement crucial for understanding stellar evolution. Traditional
estimation methods involve fitting spectra or photometry, which require
high-quality data. In recent years, machine learning has played a crucial role
in processing spectral data due to its speed, automation, and accuracy.
However, two common issues have been identified. First, most studies rely on
data with high signal-to-noise ratios (SNR > 10), leaving many poor-quality
datasets underutilized. Second, existing machine learning models, primarily
based on convolutional networks, recurrent networks, and their variants, cannot
simultaneously capture both the spatial and sequential information of spectra.
To address these challenges, we designed the Estimator Network (EstNet), an
advanced algorithm integrating multiple techniques, including Residual
Networks, Squeeze and Excitation Attention, Gated Recurrent Units, Adaptive
Loss, and Monte-Carlo Dropout Layers. We conducted parameter estimation on
5,965 poor-quality white dwarf spectra (R~1800, SNR~1.17), achieving average
percentage errors of 14.86% for effective temperature and 3.97% for surface
gravity. These results are significantly superior to other mainstream
algorithms and consistent with the outcomes of traditional theoretical spectrum
fitting methods. In the future, our algorithms will be applied for large-scale
parameter estimation on the Chinese Space Station Telescope and the Large
Synoptic Survey Telescope.
