Title: Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach Show Chinese title

Title: 快速射电暴121102脉冲检测与周期性：一种机器学习方法

Abstract: 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. Show Chinese abstract

Abstract: 我们报告在绿岸望远镜的突破聆听C波段（4-8 GHz）观测中检测到72个来自重复快速射电暴FRB 121102的新脉冲。 这些新脉冲是在2017年8月26日采集的数据中通过卷积神经网络发现的，此前已检测到21次爆发。 我们的技术结合了神经网络检测和去色散验证。 对于当前应用，我们展示了其在更高灵敏度、更低误报率和更快计算速度方面优于传统暴力去色散算法的优势。 加上之前报告的21个脉冲，这次观测标志着单次观测中FRB 121102脉冲的最高数量，总计93个脉冲，在五小时内完成，包括前30分钟内的45个脉冲。 数据点的数量揭示了脉冲通量、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Rayleigh检验来分析到达时间，借此排除了大于5.1倍模型依赖时间戳不确定性的到达时间周期性，置信度为99%。 特别是，我们排除了在质心到达时间中大于10毫秒的恒定周期，尽管发射时间中的内在周期性仍然可能是合理的。