SearcharxivSearch

arXiv subjects

Yangyang Dong

Publications and source records attributed to Yangyang Dong.

3 recordsLinked to original sources

Characterizing Orbital Parameters of Hot Subdwarf Binaries with Multiple Spectroscopic Surveys

Hot subdwarfs (HSDs) provide critical insights into the physical mechanisms governing binary evolution. In this work, we conduct a systematic analysis of 157 HSDs, selected from Gaia EDR3 and characterized using multi-survey spectroscopic data. Atmospheric parameters of these HSDs are derived via a convolutional neural network (CNN) method and template-matching method. Based on the atmospheric parameters from CNN method, these HSDs exhibit a median mass of $0.45^{+0.19}_{-0.17} M_{\odot}$ and radius of $0.18^{+0.04}_{-0.05} R_{\odot}$, consistent with earlier work. Orbital parameters of 23 systems are determined through the fitting of radial velocity data and light curves, with 11 of them being new solutions. We find that reflection-dominated binaries typically have periods longer than 0.1 d and host low-mass main-sequence companions ($\sim$ 0.2 $M_{\odot}$) with rotation-inflated radii. In contrast, binaries including an HSD and a white dwarf show very short periods ($P < 0.2$ d), with the closest systems hosting more massive white dwarfs. Most of these systems share a similar mass--period distribution with that of post-common-envelope binaries, supporting a common-envelope origin.

astro-ph.SR

Deep learning-driven atmospheric parameter prediction for hot subdwarf stars with synthetic and observed spectra

We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise ratio (SNR) levels, with each SNR level containing 11 396 synthetic spectra and 945 observed spectra. The trained deep learning models achieves mean absolute errors (AME) in predicting hot subdwarf atmospheric parameters of 730 K for effective temperature (Teff ), 0.09 dex for surface gravity (log g), and 0.03 dex for helium abundance (log(nHe/nH)), respectively, which reaches the accuracy of traditional spectral fitting methods. Utilizing the trained deep learning models and low-resolution spectra from LAMOST DR12, we confirm 1512 hot subdwarfs from the catalog of hot subdwarf candidates, of which 291 are newly identified. Our results demonstrate that the deep learning model not only achieves accuracy comparable to traditional methods in obtaining hot subdwarf atmospheric parameters, but also far exceeds them in speed and efficiency, making it particularly suitable for the analysis of large datasets of hot subdwarf spectra.

astro-ph.SR

An Improved DOA Estimation Method for a Mixture of Circular and Non-Circular Signals Based on Sparse Arrays

Sparse arrays have attracted a lot of interests recently for their capability of providing more degrees of freedom than traditional uniform linear arrays. For a mixture of circular and noncircular signals, most of the existing direction of arrival (DOA) estimation methods are based on various uniform arrays. Recently, a class of DOA estimation algorithms based on sparse arrays was developed for a mixture of circular and noncircular signals. To further improve its performance, in this work, a modified algorithm is presented, which can resolve the same number of signals, and simulation results are provided to verified its performance.

eess.SP