An adaptive parameter estimator for poor-quality spectral data of white dwarfs

TL;DR

This paper introduces EstNet, a novel machine learning model designed to accurately estimate parameters of poor-quality white dwarf spectra, outperforming existing methods and enabling large-scale stellar analysis.

Contribution

The paper presents EstNet, an advanced neural network combining residual, attention, and recurrent modules to improve parameter estimation on low-quality spectral data.

Findings

Achieved 14.86% error in temperature estimation

Achieved 3.97% error in surface gravity estimation

Outperformed mainstream algorithms on poor-quality spectra

Abstract

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.