深度学习在快速射电暴分类中的应用
Applying Deep Learning to Fast Radio Burst Classification
摘要：
即将进行的快速射电暴（FRB）调查将以非常高的占空比在天空中搜索$\sim$ 10\、$^3$波束，从而产生大量的单脉冲候选者。如果要用眼睛检查候选人，那么大量的假阳性会带来一个棘手的问题，这使其成为人工智能（AI）的一个很好的应用。我们将深度学习应用于单脉冲分类，并开发了一个分层框架，用于根据事件成为真正天体物理瞬变的概率对其进行排名。我们构建了一个树状深度神经网络（DNN），它将多个或单个数据产品作为输入（例如动态光谱和多波束检测信息），并同时对它们进行训练。我们已经建立了训练和测试集，使用来自真实望远镜的假阳性触发器，以及模拟的FRB和单脉冲。DNN的训练是为两个射电望远镜独立完成的：CHIME Pathfinder和Westerbork上的Antarctif。高准确率和召回率可以通过几千个事件的标记训练集来实现。即使触发率很高，分类也可以在图形处理单元（GPU）上非常快速地完成。该速度对于选择性电压转储或发布实时VOEvent至关重要。接下来，我们研究去分散后端是否可以完全被实时DNN分类器取代。结果表明，通过适度的卷积网络进行单次前向传播可能比蛮力去色散更快;但是每个像素的低信噪比使得这种分类器对于这个问题来说是次优的。实时自动分类可能被证明对明亮的、意想不到的信号有用，无论是现在还是在射电天文学时代，当数据量和可搜索的参数空间进一步超出我们手动检查数据的能力时，例如SKA和ngVLA。
英文摘要：
Upcoming Fast Radio Burst (FRB) surveys will search $\sim$10\,$^3$ beams on sky with very high duty cycle, generating large numbers of single-pulse candidates. The abundance of false positives presents an intractable problem if candidates are to be inspected by eye, making it a good application for artificial intelligence (AI). We apply deep learning to single pulse classification and develop a hierarchical framework for ranking events by their probability of being true astrophysical transients. We construct a tree-like deep neural network (DNN) that takes multiple or individual data products as input (e.g. dynamic spectra and multi-beam detection information) and trains on them simultaneously. We have built training and test sets using false-positive triggers from real telescopes, along with simulated FRBs, and single pulses from pulsars. Training of the DNN was independently done for two radio telescopes: the CHIME Pathfinder, and Apertif on Westerbork. High accuracy and recall can be achieved with a labelled training set of a few thousand events. Even with high triggering rates, classification can be done very quickly on Graphical Processing Units (GPUs). That speed is essential for selective voltage dumps or issuing real-time VOEvents. Next, we investigate whether dedispersion back-ends could be completely replaced by a real-time DNN classifier. It is shown that a single forward propagation through a moderate convolutional network could be faster than brute-force dedispersion; but the low signal-to-noise per pixel makes such a classifier sub-optimal for this problem. Real-time automated classification may prove useful for bright, unexpected signals, both now and in the era of radio astronomy when data volumes and the searchable parameter spaces further outgrow our ability to manually inspect the data, such as for SKA and ngVLA.
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。
