用神经网络搜索RATAN-600的快速射电暴

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing Pub Date : 2025-09-13 DOI:10.1016/j.ascom.2025.101002

D.O. Kudryavtsev , S.A. Trushkin , P.G. Tsybulev , V.A. Stolyarov

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引用次数: 0

摘要

我们提出了一种技术来搜索快速射电暴记录与宽带辐射计有很少的无线电信道。该技术应用于自2017年以来在西部地区进行的RATAN-600调查。基于EfficientNet系列模型，提出了一种用于多通道时间序列分类的一维卷积神经网络。描述了生成训练数据集所需的FRB合成信号的过程。我们实现了一个两阶段级联方案，以有效地抑制假阳性检测率。结合蟹状脉冲星的大脉冲和合成事件对训练模型进行了评价。

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Neural networks in the search for fast radio bursts with RATAN-600

We present a technique to search for fast radio bursts in records obtained with broadband radiometers having few radio channels. The technique is applied to the RATAN-600 surveys carried out at its Western Sector since the year 2017. A 1D convolutional neural network for multichannel time series classification is developed based on the EfficientNet family of models. The procedure to generate synthetic FRB signals needed for the training dataset is described. We implement a two-stage cascade scheme to effectively suppress the rate of false positive detections. Evaluation of the trained model is provided based on the synthetic events and the giant pulse of the Crab Pulsar.

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来源期刊

Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.10

自引率

8.00%

发文量

期刊介绍： Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.