论文标题
FRBSTATS:基于网络的快速无线电突发属性的基于网络的平台
FRBSTATS: A web-based platform for visualization of fast radio burst properties
论文作者
论文摘要
快速无线电爆发(FRB)的研究非常重要,并且是经过广泛研究的主题,尤其是近年来。虽然FRB的极端性质可以作为研究人员探测宇宙间培养基并研究宇宙外来方面的工具,但要频繁发现新爆发的质疑,跟踪FRB性质的挑战。我们介绍了FRBSTATS平台,该平台为已发布的FRB的开放访问目录提供了最新且用户友好的Web界面,以及观察到的事件的统计概述。该平台可以直接通过FRBSTATS API或以CSV/JSON优先数据库的形式检索基本的FRB数据,同时启用参数及其分布的绘图,以进行多种可视化。这些特征使研究人员能够与天体物理模型进行人群研究并进行比较,从而描述了这些来源背后的起源和排放机制。到目前为止,已经计算了813次爆发的推断红移估计值,提供了第一个公共数据库,其中包括从几乎所有观察到的FRB的分散量值条目以及主机红移(如果有空)中推断出的红移估计值。最后,该平台提供了一个可视化工具,该工具说明了主要突发和中继器之间的关联,并补充了瞬态名称服务器提供的基本中继器信息。在这项工作中,我们介绍了平台的结构,已建立的版本控制系统,以及保持此类开放数据库最新的策略。此外,我们介绍了一种新颖的,计算高效的基于聚类的方法,可以使数百次爆发的不受监督分类和非复制者分类,从而发现了一个新的FRB中继器。
The study of fast radio bursts (FRBs) is of great importance, and is a topic that has been extensively researched, particularly in recent years. While the extreme nature of FRBs can serve as a tool for researchers to probe the intergalactic medium and study exotic aspects of the Universe, keeping track of FRB properties is challenged by the frequent detection of new bursts. We introduce the FRBSTATS platform, which provides an up-to-date and user-friendly web interface to an open-access catalogue of published FRBs, along with a statistical overview of the observed events. The platform supports the retrieval of fundamental FRB data either directly through the FRBSTATS API, or in the form of a CSV/JSON-parsed database, while enabling the plotting of parameters and their distributions, for a variety of visualizations. These features allow researchers to conduct population studies and comparisons with astrophysical models, describing the origin and emission mechanism behind these sources. So far, the inferred redshift estimates of 813 bursts have been computed, providing the first public database that includes redshift estimates inferred from dispersion measure entries for nearly all observed FRBs, as well as host redshifts (where available). Lastly, the platform provides a visualization tool that illustrates associations between primary bursts and repeaters, complementing basic repeater information provided by the Transient Name Server. In this work, we present the structure of the platform, the established version control system, as well as the strategy for keeping such an open database up to date. Additionally, we introduce a novel, computationally-efficient, clustering-based approach that enables unsupervised classification of hundreds of bursts into repeaters and non-repeaters, resulting in the discovery of one new FRB repeater.