A White Dwarf Search Model Based on a Deep Transfer-learning Method

Abstract

White dwarfs represent the ultimate stage of evolution for over 97% of stars and play a crucial role in studies of the Milky Way’s structure and evolution. Recent years have witnessed significant progress in using deep-learning methods for identifying unique objects in large-scale data. In this paper, we present a model based on transfer learning for identifying white dwarfs. We constructed a data set using the spectra released by LAMOST DR9 and trained a convolutional neural network model. The model was then further trained using a transfer-learning approach for a binary classification model. Our final model is comprised of a seven-class classification model and a binary classification model. The testing set yielded an accuracy rate of 96.08%. Our proposed model successfully identifies 4314 of the 4479 white dwarfs published in previous papers. We applied this model to filter the 1,121,128 spectral data from the LAMOST DR9 V1 catalog. Subsequently, we obtained 6317 white dwarf candidates, of which 5014 were cross-validated and found to be known white dwarfs. We finally identified 489 new white dwarfs out of the remaining 1303 candidates, containing 377 DAs, 1 DB, 4 DZs, 1 magnetic WD, 101 DA+M binaries, and 1 DB+M binary. Our study also compared transfer-learning methods with non-transfer-learning methods, and the results show that transfer learning provides faster training speed and a higher accuracy rate. We provide the trained model and a corresponding usage program for subsequent studies.

Classification of Astronomical Spectra Based on Multiscale Partial Convolution

“…Moreover, akin to other deep-learning methodologies, MSPC-Net encounters issues related to limited interpretability. In our forthcoming endeavors, we aim to enhance MSPC-Net's capabilities through transfer learning techniques (Kolesnikov et al 2020;Tan et al 2023) to diminish its dependence on extensive labeled datasets and augment its adaptability across diverse surveys. Additionally, we are actively developing an interactive exploration tool to enhance the interpretability of deep-learning models, thereby broadening their applicability in the domain of astronomical spectroscopy.…”

“…With the availability of massive data in astronomy, the versatility, efficiency, and effectiveness of supervised learning make it a valuable tool for astronomers to analyze vast astronomical datasets, classify celestial objects, predict their properties, and uncover new insights into the universe. The adoption of supervised learning techniques, encompassing both machine-learning and deep-learning approaches, has significantly advanced the field of spectral classification and feature extraction Li & Lin 2023;Tan et al 2023;Wang et al 2023). Due to the diversity of astronomical spectra, these methods face significant challenges when applied to more generalized and comprehensive datasets, resulting in poor performance.…”