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Classifying FRB spectrograms using nonlinear dimensionality reduction techniques.
- Source :
-
Monthly Notices of the Royal Astronomical Society . Jul2023, Vol. 522 Issue 3, p4342-4351. 10p. - Publication Year :
- 2023
-
Abstract
- Fast radio bursts (FRBs) are mysterious astronomical phenomena, and it is still uncertain whether they consist of multiple types. In this study, we use two nonlinear dimensionality reduction algorithms – Uniform Manifold Approximation and Projection (UMAP) and t-distributed stochastic neighbour embedding (t-SNE) – to differentiate repeaters from apparently non-repeaters in FRBs. Based on the first Canadian Hydrogen Intensity Mapping Experiment (CHIME) FRB catalogue, these two methods are applied to standardized parameter data and image data from a sample of 594 sub-bursts and 535 FRBs, respectively. Both methods are able to differentiate repeaters from apparently non-repeaters. The UMAP algorithm using image data produces more accurate results and is a more model-independent method. Our result shows that in general repeater clusters tend to be narrowband, which implies a difference in burst morphology between repeaters and apparently non-repeaters. We also compared our UMAP predictions with the CHIME/FRB discovery of six new repeaters, the performance was generally good except for one outlier. Finally, we highlight the need for a larger and more complete sample of FRBs. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DIMENSIONAL reduction algorithms
*DIMENSION reduction (Statistics)
*SPECTROGRAMS
Subjects
Details
- Language :
- English
- ISSN :
- 00358711
- Volume :
- 522
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Monthly Notices of the Royal Astronomical Society
- Publication Type :
- Academic Journal
- Accession number :
- 163741986
- Full Text :
- https://doi.org/10.1093/mnras/stad1304