论文标题

搜索绿色银行望远镜的技术签名大约31颗阳光般的星星,为1.15-1.73 GHz

A Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz

论文作者

Margot, Jean-Luc, Pinchuk, Pavlo, Geil, Robert, Alexander, Stephen, Arora, Sparsh, Biswas, Swagata, Cebreros, Jose, Desai, Sanjana Prabhu, Duclos, Benjamin, Dunne, Riley, Lin, Kristy Kwan, Goel, Shashwat, Gonzales, Julia, Gonzalez, Alexander, Jain, Rishabh, Lam, Adrian, Lewis, Briley, Lewis, Rebecca, Li, Grace, MacDougall, Mason, Makarem, Christopher, Manan, Ivan, Molina, Eden, Nagib, Caroline, Neville, Kyle, O'Toole, Connor, Rockwell, Valerie, Rokushima, Yoichiro, Romanek, Griffin, Schmidgall, Carlyn, Seth, Samar, Shah, Rehan, Shimane, Yuri, Singhal, Myank, Tokadjian, Armen, Villafana, Lizvette, Wang, Zhixian, Yun, In, Zhu, Lujia, Lynch, Ryan S.

论文摘要

我们在2018年4月和2019年4月对技术签名进行了搜索,直径为100 m的绿色银行望远镜的L波段接收器(1.15-1.73 GHz)。这些观察结果集中在银河平面附近31个阳光恒星周围的区域上。我们介绍了在此数据集中搜索窄带信号的结果以及对数据处理管道的改进。具体而言,我们应用了一个改进的候选信号检测程序,该过程依赖于信号功率的地形突出性,该过程几乎使一些先前分析的数据集的信号检测计数翻了一番。我们还改进了去除大多数射频干扰(RFI)的原始滤波器,以确保它们在单独的扫描中观察到的唯一链接信号。我们进行了初步的信号注入和恢复分析,以测试管道的性能。我们发现,我们的管道在接收器的可用频率范围内恢复了93%的注射信号,如果我们排除具有密度RFI的区域,则恢复了98%。在此分析中,有99.73%的恢复信号正确分类为技术签名候选者。我们改进的数据处理管道分类为超过99.84%的2600万个信号,该信号被发现为RFI。在其余候选人中,在已知的RFI频率区域之外检测到4539个。对其余的候选人进行了视觉检查,并证明具有人为性质。我们的搜索在端到端灵敏度,频率漂移率覆盖率和单位单位集成时间单位带宽计数方面与其他最新搜索进行了比较。

We conducted a search for technosignatures in April of 2018 and 2019 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused on regions surrounding 31 Sun-like stars near the plane of the Galaxy. We present the results of our search for narrowband signals in this data set as well as improvements to our data processing pipeline. Specifically, we applied an improved candidate signal detection procedure that relies on the topographic prominence of the signal power, which nearly doubles the signal detection count of some previously analyzed data sets. We also improved the direction-of-origin filters that remove most radio frequency interference (RFI) to ensure that they uniquely link signals observed in separate scans. We performed a preliminary signal injection and recovery analysis to test the performance of our pipeline. We found that our pipeline recovers 93% of the injected signals over the usable frequency range of the receiver and 98% if we exclude regions with dense RFI. In this analysis, 99.73% of the recovered signals were correctly classified as technosignature candidates. Our improved data processing pipeline classified over 99.84% of the ~26 million signals detected in our data as RFI. Of the remaining candidates, 4539 were detected outside of known RFI frequency regions. The remaining candidates were visually inspected and verified to be of anthropogenic nature. Our search compares favorably to other recent searches in terms of end-to-end sensitivity, frequency drift rate coverage, and signal detection count per unit bandwidth per unit integration time.

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