FAST-FREX：the FAST dataset for Fast Radio bursts Exploration

Fast Radio Burst (FRB) is a high-energy astrophysical phenomenon of short duration that plays a significant role in studying the distribution and evolution of matter in the universe. FRB can only be observed through radio telescopes, which produce extensive amounts of observational data, making the search for FRB challenging. Traditional techniques for searching FRB are computationally expensive, time-consuming, and may fail to detect weak signals, leading to decreased effectiveness. Consequently, machine learning algorithms are employed to mitigate the challenges of searching FRB. This poses a challenge since only a limited number of incomplete or inadequate FRB datasets are available for machine learning algorithms. Hence, providing substantial and authentic datasets is crucial in aiding machine learning algorithms to detect FRB.The FAST dataset for Fast Radio bursts EXploration (FAST-FREX) is obtained from observation data collected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our dataset comprises 600 positive samples of observed FRB signals from multiple sources and 1000 negative samples of noise and Radio Frequency Interference (RFI). We created our dataset by using the observational data of FRB20121102, FRB20180301 and FRB20201124. These observations gathered data using 4096 frequency channels over 1.05 GHz to 1.45 GHz, with 0.122 MHz frequency resolution. These channels recorded four polarization signals. FRB20121102 has a 98.304 μs sampling rate, while others have a 49.152 μs sampling rate. The raw observation data was stored in FITS format and divided into blocks of 128 or 256 time samples. The samples were recorded in consecutive lines (or sub-integrations) in a file with 1024 samples per sub-integration.The dataset comprises two file types: sample files and parameter description files. The sample files, stored in FITS format, contain pre-cropped observation data. Among them are 600 positive sample files containing FRB signals and 1000 negative sample files containing RFI and noise. The FRB20121102 sample file has a time sampling point of 60 * 1024, while other sources' sample files have 120 * 1024. Moreover, the sample file's number of polarization channels is reduced to one, which differs from the original data. The size of each file is approximately 244 MB or 488 MB, depending on its time sampling rate. Meanwhile, parameter description files are stored in CSV format to record various FRB parameters for each positive sample file. Each positive sample file contains only one FRB signal and its parameters are recorded in a corresponding parameter description file specific to a fixed FRB source. In constrast, negative sample files do not have a corresponding parameter description file.

快速射电暴（Fast Radio Burst, FRB）是一种短时标的高能天体物理现象，在研究宇宙中物质的分布与演化方面具有重要意义。FRB仅能通过射电望远镜观测，而望远镜会产生海量观测数据，使得FRB搜寻工作极具挑战性。传统搜寻FRB的技术计算成本高、耗时久，且可能无法检测到弱信号，导致效率降低。因此，人们采用机器学习算法来缓解FRB搜寻的挑战。但这又带来了新的问题：可供机器学习算法使用的FRB数据集数量有限，且多不完整或质量不足。因此，提供充足且真实的数据集对于辅助机器学习算法检测FRB至关重要。

FAST快速射电暴探索数据集（FAST-FREX）来源于500米口径球面射电望远镜（Five-hundred-meter Aperture Spherical Radio Telescope, FAST）采集的观测数据。该数据集包含来自多个源的600个观测到的FRB信号正样本，以及1000个噪声和射频干扰（Radio Frequency Interference, RFI）负样本。我们利用FRB20121102、FRB20180301和FRB20201124的观测数据构建了该数据集。

这些观测使用4096个频率通道（覆盖1.05 GHz至1.45 GHz频段，频率分辨率为0.122 MHz）采集数据，并记录了四个偏振信号。FRB20121102的采样率为98.304微秒，其余源的采样率为49.152微秒。原始观测数据以FITS格式存储，并被划分为128或256个时间样本的块。样本以连续行（或子积分）的形式记录在文件中，每个子积分包含1024个样本。

该数据集包含两种文件类型：样本文件和参数描述文件。样本文件以FITS格式存储，包含预裁剪的观测数据，其中包括600个含FRB信号的正样本文件和1000个含RFI及噪声的负样本文件。FRB20121102的样本文件包含60×1024个时间采样点，而其他源的样本文件包含120×1024个时间采样点。此外，样本文件的偏振通道数被缩减为1个，这与原始数据不同。每个文件的大小约为244 MB或488 MB，具体取决于其时间采样率。

同时，参数描述文件以CSV格式存储，用于记录每个正样本文件对应的各类FRB参数。每个正样本文件仅包含一个FRB信号，其参数记录在对应于固定FRB源的参数描述文件中。相比之下，负样本文件没有对应的参数描述文件。

搜集汇总

背景与挑战

背景概述

FAST-FREX数据集是基于FAST望远镜观测数据构建的快速射电暴（FRB）研究数据集，旨在支持机器学习算法在FRB检测中的应用。它包含600个来自多个FRB源的正样本和1000个噪声及射频干扰负样本，数据以FITS格式存储，并配有CSV参数文件记录FRB细节，覆盖频率范围1.05-1.45 GHz，具有高分辨率采样特点。