使用GBT进行实时FRB搜索的初始结果
Initial results from a realtime FRB search with the GBT
摘要：
本文介绍了罗伯特·C·格林波斯特快速射电暴（FRB）探测系统的数据分析管道、调试观测和初步结果。Byrd Green Bank Telescope（GBT）是Surnis等人提出的一种与其他项目同步观测的21厘米接收器。该管道利用最先进的深度学习分类器来筛选大量的假阳性单脉冲候选者，这些候选者主要来自射频干扰。到目前为止，在我们总共156.5天的观测中，我们已经探测到了来自20个已知射电望远镜的单个脉冲，这为系统性能提供了很好的验证。我们还表明，通过盲注分析，我们的管道是完整的信噪比阈值为12。取决于观测模式，这转化为在0.14- 0.89 μ π ι范围内的峰值通量灵敏度。虽然迄今为止还没有发现FRB，但我们已经使用我们的结果更新了Lawrence等人的分析，以将FRB全天速率限制在峰值通量密度1~Jy以上的每天$1140^{+200}_{-180}$。我们还约束源计数指数$α=0.83\pm0.06$，这表明源计数分布比标准蜡烛的欧几里得分布（其中$α=1.5$）预期的要平坦得多。我们讨论这一结果的背景下的FRB红移和光度分布。最后，我们预测GREENBURST的检测率，以及其他正在进行和计划中的FRB实验。
英文摘要：
We present the data analysis pipeline, commissioning observations and initial results from the GREENBURST fast radio burst (FRB) detection system on the Robert C. Byrd Green Bank Telescope (GBT) previously described by Surnis et al. which uses the 21~cm receiver observing commensally with other projects. The pipeline makes use of a state-of-the-art deep learning classifier to winnow down the very large number of false positive single-pulse candidates that mostly result from radio frequency interference. In our observations totalling 156.5 days so far, we have detected individual pulses from 20 known radio pulsars which provide an excellent verification of the system performance. We also demonstrate, through blind injection analyses, that our pipeline is complete down to a signal-to-noise threshold of 12. Depending on the observing mode, this translates to peak flux sensitivities in the range 0.14--0.89~Jy. Although no FRBs have been detected to date, we have used our results to update the analysis of Lawrence et al. to constrain the FRB all-sky rate to be $1140^{+200}_{-180}$ per day above a peak flux density of 1~Jy. We also constrain the source count index $α=0.83\pm0.06$ which indicates that the source count distribution is substantially flatter than expected from a Euclidean distribution of standard candles (where $α=1.5$). We discuss this result in the context of the FRB redshift and luminosity distributions. Finally, we make predictions for detection rates with GREENBURST, as well as other ongoing and planned FRB experiments.
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