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arXiv 1809.03043astro-ph.HEastro-ph.IM
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快速射电暴121102脉冲检测和周期性:一种机器学习方法

Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach

Yunfan Gerry Zhang, Vishal Gajjar, Griffin Foster, Andrew Siemion, James Cordes, Casey Law, Yu Wang

摘要:

我们报告了72个新的脉冲,从重复的快速射电暴FRB 121102在突破听C波段(4-8 GHz)的观测在绿岸望远镜的检测。新的脉冲是在2017年8月26日采集的数据中使用卷积神经网络发现的,此前已经检测到21次爆发。我们的技术结合了神经网络检测与去色散验证。对于当前的应用程序,我们证明了它的优势,在更高的灵敏度,更低的假阳性率,更快的计算速度比传统的暴力dedission- persion算法。加上之前报道的21个脉冲,这次观测标志着FRB 121102脉冲的最高数量,在5个小时内总共有93个脉冲,包括前30分钟内的45个脉冲。数据点的数量揭示了脉冲能量密度、脉冲检测率和脉冲频率结构的趋势。我们引入了一种新的周期性搜索技术,基于瑞利测试,来分析到达时间,与此同时,我们排除了99%的置信度周期的到达时间大于5.1倍的模型相关的时间戳不确定性。特别是,我们排除了恒定的周期>10毫秒的重心到达时间,虽然内在的周期性的时间发射仍然是合理的。

英文摘要:

We report the detection of 72 new pulses from the repeating fast radio burst FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green Bank Telescope. The new pulses were found with a convolutional neural network in data taken on August 26, 2017, where 21 bursts have been previously detected. Our technique combines neural network detection with dedispersion verification. For the current application we demonstrate its advantage over a traditional brute-force dedis- persion algorithm in terms of higher sensitivity, lower false positive rates, and faster computational speed. Together with the 21 previously reported pulses, this observa- tion marks the highest number of FRB 121102 pulses from a single observation, total- ing 93 pulses in five hours, including 45 pulses within the first 30 minutes. The number of data points reveal trends in pulse fluence, pulse detection rate, and pulse frequency structure. We introduce a new periodicity search technique, based on the Rayleigh test, to analyze the time of arrivals, with which we exclude with 99% confidence pe- riodicity in time of arrivals with periods larger than 5.1 times the model-dependent time-stamp uncertainty. In particular, we rule out constant periods >10 ms in the barycentric arrival times, though intrinsic periodicity in the time of emission remains plausible.

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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