通过模拟FRB的实时注入估计UTMOST的快速瞬态检测流水线效率
Estimating fast transient detection pipeline efficiencies at UTMOST via real-time injection of mock FRBs
摘要：
正在多个观测站使用不同的探测管道进行专门的调查，以发现更多的快速射电暴（FRB）。理解检测算法的效率和调查完整性函数对于实现对基础FRB群体属性的无偏估计是重要的。实现系统端到端测试的一种方法是在实时数据流中注入模拟FRB并盲目搜索它们。模拟FRB注入对于基于机器学习的分类器特别有效，因为分析特征是不切实际的。我们描述了一个实时模拟FRB注入系统在升级后的Molonglo天文台合成望远镜（UTMOST）的第一种实现，并提出了一组20,000模拟FRB注入我们的结果。注射产生了明确的洞察到检测效率，并提供了一个调查的完整性功能的脉冲宽度，通量和DM。模拟FRB恢复均匀的效率在整个范围内注入DM，但恢复分数被发现是一个强大的功能的宽度和信噪比（SNR）。对于低宽度（$\lesssim 20$ ms）和高SNR（$\gtrsim$ 9），恢复是高度有效的，恢复分数超过90%。我们发现，射频干扰的存在下，导致恢复的SNR值被系统地降低高达20%相比，注入值。我们发现，对于UTMOST采用的基于机器学习的分类器，更广泛的FRB变得越来越难以恢复。我们鼓励其他观测站实施实时注入设置，以对其调查进行类似测试。
英文摘要：
Dedicated surveys using different detection pipelines are being carried out at multiple observatories to find more Fast Radio Bursts (FRBs). Understanding the efficiency of detection algorithms and the survey completeness function is important to enable unbiased estimation of the underlying FRB population properties. One method to achieve end-to-end testing of the system is by injecting mock FRBs in the live data-stream and searching for them blindly. Mock FRB injection is particularly effective for machine-learning-based classifiers, for which analytic characterisation is impractical. We describe a first-of-its-kind implementation of a real-time mock FRB injection system at the upgraded Molonglo Observatory Synthesis Telescope (UTMOST) and present our results for a set of 20,000 mock FRB injections. The injections have yielded clear insight into the detection efficiencies and have provided a survey completeness function for pulse width, fluence and DM. Mock FRBs are recovered with uniform efficiency over the full range of injected DMs, however the recovery fraction is found to be a strong function of the width and Signal-to-Noise (SNR). For low widths ($\lesssim 20$ ms) and high SNR ($\gtrsim$ 9) the recovery is highly effective with recovery fractions exceeding 90%. We find that the presence of radio frequency interference causes the recovered SNR values to be systematically lower by up to 20% compared to the injected values. We find that wider FRBs become increasingly hard to recover for the machine-learning-based classifier employed at UTMOST. We encourage other observatories to implement live injection set-ups for similar testing of their surveys.
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。
