ELM Survey 南天区巡天. II. 二十四个新的低质量白矮星双星
The ELM Survey South. II. Two dozen new low mass white dwarf binaries
AI总结：
本文报告了针对南天区低质量白矮星双星的光谱巡天结果，基于 Gaia 数据识别出 28 个新双星并测定其参数，使南天区 ELM Survey 双星样本增加 64%，并指出未来时域巡天将进一步完善全天区种群。
摘要：
我们展示了正在进行的目标为低质量白矮星双星的光谱巡天结果，重点关注南天区。我们使用了基于 Gaia DR2 和 eDR3 的选择方法，识别出 28 个新双星，包括 19 个新的极低质量白矮星，一个短周期、可能发生掩食的 DABZ，以及两个潜在的 LISA 双星。我们基于光谱后续观测，给出了每个新双星的轨道和大气参数。我们的新双星中有四个在 TESS 2 分钟采样数据中表现出周期性测光变化，包括一个新的掩食双线分光双星。另外三个在 ZTF 中表现出周期性测光变化，包括一个新的掩食双星。基于对我们的高速测光后续观测的光变曲线建模，我们给出了这些 ZTF 变源的倾角和缩放后的各组件半径估计值。我们的观测已将南天区识别出的 ELM Survey 双星样本增加到 41 个，增加了 64%。未来的时域巡天，如 BlackGEM 和 Vera C. Rubin Observatory Legacy Survey of Space and Time，将高效识别南天区的测光变源，并显著增加南天区低质量白矮星双星的种群数量，从而促成这些系统更完整的全天区种群。
英文摘要：
We present the results from our ongoing spectroscopic survey targeting low mass white dwarf binaries, focusing on the southern sky. We used a Gaia DR2 and eDR3 based selection and identified 28 new binaries, including 19 new extremely low mass white dwarfs, one short period, likely eclipsing, DABZ, and two potential LISA binaries. We present orbital and atmospheric parameters for each new binary based on our spectroscopic follow-up. Four of our new binaries show periodic photometric variability in the TESS 2-minute cadence data, including one new eclipsing double-lined spectroscopic binary. Three others show periodic photometric variability in ZTF, including one new eclipsing binary. We provide estimates for the inclinations and scaled component radii for these ZTF variables, based on light curve modeling to our high-speed photometric follow-up observations. Our observations have increased the sample of ELM Survey binaries identified in the southern sky to 41, an increase of 64%. Future time domain surveys, such as BlackGEM and the Vera C. Rubin Observatory Legacy Survey of Space and Time, will efficiently identify the photometric variables in the southern sky and significantly increase the population of southern sky low mass white dwarf binaries, leading to a more complete all-sky population of these systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
