AI 中文总结
研究从DESI光谱中识别激变变星，利用机器学习技术搜索光谱，识别出众多CVs，包括新的亚型，确认了候选体并获取轨道周期，给出空间密度估计，还增加了一类特殊CVs的数量。
AI 中文摘要
大多数激变变星（CVs）是在爆发时被发现的，这对很少爆发或从不爆发的CVs产生了固有选择偏差。凭借光谱特征发现的CVs对于抵消这种偏差尤为重要。我们利用一种成熟的机器学习技术，在暗能量光谱巡天（DESI）获得的98966000个光谱中搜索此类CVs。DESI观测比以前的光谱巡天更深，我们已识别出1029个CVs，其中221个是新的，包括10个AM CVn亚型。我们已通过光谱确认了441个CV候选体，并获得了84个新的或改进的轨道周期。我们基于这些新数据给出了修订后的空间密度估计。我们还在一类有趣的CVs（其吸积表现出特殊变化）的8个已知例子中又增加了10个。
英文摘要
Most cataclysmic variables (CVs) are discovered when they have an outburst generating an inherent selection bias against CVs that rarely, or never, outburst. CVs discovered by virtue of their spectroscopic characteristics are particularly valuable to offset this bias and we have used an established machine-learning technique to assist in searching 98 966 000 spectra obtained by the Dark Energy Spectroscopic Survey (DESI) to find such CVs. DESI observations are much deeper than previous spectroscopic surveys and we have identified 1029 CVs, 221 of which are new including ten of the AM CVn subtype. We have spectroscopically confirmed 441 CV candidates and obtained 84 new or improved orbital periods. We present revised space density estimates based upon this new data. We have also added ten more to the eight known examples of an intriguing class of CVs which exhibit peculiar changes in accretion.
Comments28 pages, 35 figures plus extensive supplementary information. Submitted to MNRAS
