arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.05265astro-ph.EPastro-ph.IM
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用1.15- 1.73GHz绿岸望远镜寻找31颗类日恒星的技术特征

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

Jean-Luc Margot, Pavlo Pinchuk, Robert Geil, Stephen Alexander, Sparsh Arora, Swagata Biswas, Jose Cebreros, Sanjana Prabhu Desai, Benjamin Duclos, Riley Dunne,… 展开作者

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

摘要:

我们在2018年4月和2019年4月使用100米直径的绿岸望远镜的L波段接收器(1.15-1.73 GHz)进行了技术签名搜索。这些观测集中在银河系平面附近的31颗类太阳恒星周围的区域。我们提出了我们在这个数据集中搜索窄带信号的结果,以及我们的数据处理管道的改进。具体来说,我们应用了一种改进的候选信号检测程序,该程序依赖于信号功率的地形突出性,这几乎使一些先前分析的数据集的信号检测计数增加了一倍。我们还改进了消除大多数射频干扰(RFI)的源方向滤波器,以确保它们唯一地链接在单独扫描中观察到的信号。我们进行了初步的信号注入和恢复分析,以测试我们的管道的性能。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%的注入信号,如果排除RFI密集的区域,则恢复了98%。在该分析中,99.73%的恢复信号被正确地分类为技术特征候选者。我们改进的数据处理管道将我们数据中检测到的约2600万个信号中的99.84%分类为RFI。在其余候选者中,有4539个是在已知RFI频率区域之外检测到的。对其余的候选物进行了目视检查,并核实为人为性质。我们的搜索相媲美的其他最近的搜索方面的端到端的灵敏度,频率漂移率的覆盖范围,和信号检测计数每单位带宽每单位积分时间。

英文摘要:

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.

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。

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