J/MNRAS/509/1227 Hidden repeating fast radio bursts (Chen+, 2022)
Uncloaking hidden repeating fast radio bursts with unsupervised machine
learning.
Chen B.H., Hashimoto T., Goto T., Kim S.J., Santos D.J.D., On A.Y.L.,
Lu T.-Y., Hsiao T.Y.-Y.
<Mon. Not. R. Astron. Soc. 509, 1227-1236>
=2022MNRAS.509.1227C 2022MNRAS.509.1227C (SIMBAD/NED BibCode)
ADC_Keywords: Radio sources
Keywords: methods: data analysis
Abstract:
The origins of fast radio bursts (FRBs), astronomical transients with
millisecond time-scales, remain unknown. One of the difficulties stems
from the possibility that observed FRBs could be heterogeneous in
origin; as some of them have been observed to repeat, and others have
not. Due to limited observing periods and telescope sensitivities,
some bursts may be misclassified as non- repeaters. Therefore, it is
important to clearly distinguish FRBs into repeaters and
non-repeaters, to better understand their origins. In this work, we
classify repeaters and non-repeaters using unsupervised machine
learning, without relying on expensive monitoring observations. We
present a repeating FRB recognition method based on the Uniform
Manifold Approximation and Projection (UMAP). The main goals of this
work are to: (i) show that the unsupervised UMAP can classify
repeating FRB population without any prior knowledge about their
repetition, (ii) evaluate the assumption that non-repeating FRBs are
contaminated by repeating FRBs, and (iii) recognize the FRB repeater
candidates without monitoring observations and release a corresponding
catalogue. We apply our method to the Canadian Hydrogen Intensity
Mapping Experiment Fast Radio Burst (CHIME/FRB) data base. We found
that the unsupervised UMAP classification provides a repeating FRB
completeness of 95 per cent and identifies 188 FRB repeater source
candidates from 474 non-repeater sources. This work paves the way to a
new classification of repeaters and non-repeaters based on a single
epoch observation of FRBs.
Description:
We provide an FRB repeater candidate catalogue based on CHIME/FRB data
base.
File Summary:
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FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table.dat 62 593 CHIME FRB ML classification
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Byte-by-byte Description of file: table.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 12 A12 --- TNSName Name ID provided for each burst,
FRBYYYYMMDDA (1)
14- 15 I2 --- Class [-1/1]? FRB classification provides by
machine learning model (2)
17- 34 A18 --- Group Clustering result for machine learning model
output, see Fig.3 of the paper
36- 48 F13.9 --- Xpos The x coordinate of the low dimensional
projection of each FRB, see Fig.2, 3, 4 of
the paper
51- 62 F12.9 --- Ypos The y coordinate of the low dimensional
projection of each FRB, see Fig.2, 3, 4 of
the paper
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Note (1): The same repeating FRB source has multiple TNSName due to its
repetition. A non-repeating FRB has the single TNSName. The same TNSName
can have multiple sub-bursts. Therefore, multiple rows with the same TNSName
is due to the sub-bursts.
Note (2): Classification as follows:
1 = repeating FRB
0 = repeating FRB candidate
-1 = non-repeating FRB
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 14-Aug-2024