Machine learning classification of repeating FRBs from FRB 121102

被引:2
|
作者
Raquel, Bjorn Jasper R. [1 ,2 ,3 ]
Hashimoto, Tetsuya [2 ]
Goto, Tomotsugu [4 ]
Chen, Bo Han [4 ,5 ,6 ]
Uno, Yuri [2 ]
Hsiao, Tiger Yu-Yang [4 ,7 ]
Kim, Seong Jin [4 ]
Ho, Simon C-C [4 ,8 ]
机构
[1] Rizal Technol Univ, Dept Earth & Space Sci, Boni Ave, Mandaluyong City 1550, Metro Manila, Philippines
[2] Natl Chung Hsing Univ, Dept Phys, 145,Xingda Rd, Taichung 40227, Taiwan
[3] Univ Philippines, Natl Inst Phys, Coll Sci, Quezon City 1101, Metro Manila, Philippines
[4] Natl Tsing Hua Univ, Inst Astron, 101,Sect 2,Kuang Fu Rd, Hsinchu 30013, Taiwan
[5] Natl Tsing Hua Univ, Dept Phys, 101, Sect 2,Kuang Fu Rd, Hsinchu 30013, Taiwan
[6] Seoul Natl Univ, Grad Sch Data Sci, 1,Gwanak Ro, Seoul, South Korea
[7] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA
[8] Australian Natl Univ, Res Sch Astron & Astrophys, Canberra, ACT 2611, Australia
关键词
methods: data analysis; stars: magnetars; stars: neutron; (transients:) fast radio bursts; FAST RADIO-BURST; SUPERLUMINOUS SUPERNOVAE; HOST GALAXY; DURATION; MAGNETAR; LONG;
D O I
10.1093/mnras/stad1942
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Fast radio bursts (FRBs) are mysterious bursts in the millisecond time-scale at radio wavelengths. Currently, there is little understanding about the classification of repeating FRBs, based on difference in physics, which is of great importance in understanding their origin. Recent works from the literature focus on using specific parameters to classify FRBs to draw inferences on the possible physical mechanisms or properties of these FRB subtypes. In this study, we use publicly available 1652 repeating FRBs from FRB 121102 detected with the Five-hundred-metre Aperture Spherical Telescope (FAST), and studied them with an unsupervised machine learning model. By fine-tuning the hyperparameters of the model, we found that there is an indication for four clusters from the bursts of FRB 121102 instead of the two clusters ('Classical' and 'Atypical') suggested in the literature. Wherein, the 'Atypical' cluster can be further classified into three sub-clusters with distinct characteristics. Our findings show that the clustering result we obtained is more comprehensive not only because our study produced results which are consistent with those in the literature but also because our work uses more physical parameters to create these clusters. Overall, our methods and analyses produced a more holistic approach in clustering the repeating FRBs of FRB 121102.
引用
收藏
页码:1668 / 1691
页数:24
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