A new code for low-resolution spectral identification of white dwarf binary candidates

¹ School of Physics and Astronomy, Sun Yat-sen University, Zhuhai 519082, China
² CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area, Zhuhai 519082, China
³ School of Electrical and Electronic Engineering, Anhui Science and Technology University, Bengbu, Anhui 233030, China
⁴ School of Natural Sciences, Institute for Advanced Study, Princeton, 1 Einstein Drive, NJ 08540, USA
⁵ Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing 102206, China
⁶ School of Physics and Astronomy, Beijing Normal University, Beijing 100875, China
⁷ MOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics, Frontiers Science Center for TianQin, Gravitational Wave Research Center of CNSA, Sun Yat-sen University, Zhuhai 519082, China

Received: 28 February 2024
Accepted: 31 July 2024

Abstract

Context. Close white dwarf binaries (CWDBs) are considered to be progenitors of several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic variables). These violent events are broadly used in studies of general relativity and cosmology. However, obtaining precise stellar parameter measurements for both components of CWDBs is a challenging task given their low luminosities, swift time variation, and complex orbits. High-resolution spectra (R > 20 000) are preferred but expensive, resulting in a sample size that is insufficient for robust population study. Recently, studies have shown that the more accessible low-resolution (R ~ 2000) spectra (LRS) may also provide enough information for spectral decomposition. To release the full potential of the less expensive low-resolution spectroscopic surveys, and thus greatly expand the CWDB sample size, it is necessary to develop a robust pipeline for spectra decomposition and analysis.

Aims. We aim to develop a spectroscopic fitting program for white dwarf binary systems based on photometry, LRS, and stellar evolutionary models. The outputs include stellar parameters of both companions in the binary including effective temperature, surface gravity, mass, radius, and metallicity in the case of MS stars.

Methods. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. Characteristic spectral lines were used to decompose the spectrum of each component. The best-fit stellar parameters were obtained by finding the least χ² solution to these feature lines and the continuum simultaneously. Compared to previous studies, our code is innovative in the following aspects: (1) implementing a sophisticated binary decomposition technique in LRS for the first time; (2) using flux-calibrated spectra instead of photometry plus spectral lines, in which the latter requires multi-epoch observations; (3) applying an ANN in binary decomposition, which significantly improves the efficiency and accuracy of generated spectra.

Results. We demonstrate the reliability of our code with two well-studied CWDBs, WD 1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly identified CWDB candidates, most of which are fitted with double component models for the first time. Our estimates agree with previous results for the common stars and follow the statistical distribution in the literature.

Conclusions. We provide a robust program for fitting binary spectra of various resolutions. Its application to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring.