标题： 利用机器学习分类重新审视FRB 20121102A的神秘起源 显示英文标题

标题： Revisiting the Mysterious Origin of FRB 20121102A with Machine-learning Classification

摘要： 快速射电暴（FRBs）是来自宇宙的毫秒级无线电波。尽管已有超过50种物理模型被提出，但FRB辐射的起源和物理机制仍然未知。FRB的分类是理解其机制的主要方法之一，但以往的研究仅使用少数几个观测参数（如流量和持续时间）进行传统分类，这可能并不完整。为了解决这个问题，我们使用了一种无监督机器学习模型——均匀流形近似与投影（UMAP），同时处理七个参数，包括振幅、线性时漂、时间持续时间、中心频率、带宽、归一化能量和流量。我们测试了这种方法对由阿雷西博望远镜探测到的FRB 20121102A的977个子暴的适用性。我们的机器学习分析识别出了五个不同的簇，表明可能存在多种不同的物理机制，这些机制负责FRB 20121102A源所观测到的FRBs。发射区域的几何形状和FRB信号的传播效应也可能导致这些明显的簇。当专用的射电望远镜，如平方公里阵列（SKA）或台湾的“蓬勃宇宙射电巡天望远镜”（BURSTT）发现更多FRBs时，这项研究将成为未来FRB分类的基准。 显示英文摘要

摘要： Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection (UMAP) to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the Square Kilometer Array (SKA) or Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) discover more FRBs than before.