一种用于白矮星双星候选体低分辨率光谱证认的新代码
A new code for low-resolution spectral identification of white dwarf binary candidates
AI总结：
本文提出一种基于人工神经网络和最小χ²拟合的低分辨率光谱分解代码，用于高效测定密近白矮星双星候选体的恒星参数，并通过已知和新增样本验证其可靠性，以扩大CWDB统计样本。
摘要：
密近白矮星双星（CWDBs）被认为是若干奇特天文现象（例如Ia型超新星、激变变星）的前身天体。这些剧烈事件被广泛用于广义相对论和宇宙学研究。然而，由于CWDBs光度低、时变迅速且轨道复杂，获取其两个子星的精确恒星参数测量是一项具有挑战性的任务。高分辨率光谱（R>20 000）虽然更受青睐但成本高昂，导致样本量不足以支持稳健的族群研究。为了释放成本较低的低分辨率光谱巡天的全部潜力，从而大幅扩大CWDB样本量，有必要开发一套稳健的光谱分解与分析流程。我们使用人工神经网络（ANN）构建了DA/DB白矮星和主序星的光谱生成器。通过同时对这些特征谱线和连续谱求最小χ²解，获得最佳拟合恒星参数。我们利用两个被充分研究的CWDBs——WD 1534+503和PG 1224+309——证明了我们代码的可靠性。我们还估算了14个新证认的CWDB候选体的恒星参数，其中大多数是首次用双成分模型进行拟合。我们的估算结果与已发表文献中共同恒星的前人结果一致，并符合文献中的统计分布。将我们的代码应用于大量白矮星双星候选体，将为恒星演化研究和未来引力波监测提供重要的统计样本。
英文摘要：
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. 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. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. The best-fit stellar parameters were obtained by finding the least $χ^2$ solution to these feature lines and the continuum simultaneously. 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. The application of our code to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring.
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