Composite periodogram analysis for low-event-count fast radio burst repeaters Article Swipe
Murthadza Aznam
,
Zainal Abidin
,
Norsiah Hashim
,
Tomotsugu Goto
,
T. Hashimoto
,
Muhammad Hassan Zakie
,
Matdhesh Kumar Jayaganthan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1051/0004-6361/202452508
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1051/0004-6361/202452508
Context. Fast radio bursts (FRBs) are a class of transients characterised by their millisecond-scale duration and relatively high dispersion measures. Some FRBs have been observed to repeat. For such repeating FRBs, measuring the period is key to identifying their physical mechanisms. However, periods have only been measured for two FRBs – FRB 20112002A and FRB 20180916B – because most repeating FRBs have a low event count, making it challenging to measure their periods. Aims. We aim to introduce a composite periodogram strategy designed to measure periods of repeating FRBs out of low event counts. Methods. We combined the χ 2 periodogram with the inactivity fraction periodogram ( F 0 ) to maximise the detection likelihood and minimise noise. Our approach was validated using FRB 20180916B, whereby we successfully recovered the known period with as few as five events. We then applied this method to 17 FRBs whose event counts range from three to 12. Results. A candidate period is identified in FRB 20190804E, FRB 20190915D, FRB 20200223B, FRB 20201130A, and FRB 20201221B, with a probability of chance coincidence ≤0.050. Additionally, we did further checks to eliminate false positives and concluded that the candidate periods for FRB 20190804E (168.39 −0.07 +3.86 days) and FRB 20201130A (11.38 −0.10 +0.10 days) are likely close to its true period. These values provide a basis for informed follow-up observations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/0004-6361/202452508
- OA Status
- hybrid
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407688442
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https://openalex.org/W4407688442Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1051/0004-6361/202452508Digital Object Identifier
- Title
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Composite periodogram analysis for low-event-count fast radio burst repeatersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-02-18Full publication date if available
- Authors
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Murthadza Aznam, Zainal Abidin, Norsiah Hashim, Tomotsugu Goto, T. Hashimoto, Muhammad Hassan Zakie, Matdhesh Kumar JayaganthanList of authors in order
- Landing page
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https://doi.org/10.1051/0004-6361/202452508Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1051/0004-6361/202452508Direct OA link when available
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Physics, Astrophysics, Periodogram, Composite number, Event (particle physics), Astronomy, Statistics, Algorithm, Computer science, MathematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.introduce | 77 |
| abstract_inverted_index.measures. | 19 |
| abstract_inverted_index.measuring | 31 |
| abstract_inverted_index.positives | 187 |
| abstract_inverted_index.recovered | 128 |
| abstract_inverted_index.repeating | 29, 59, 87 |
| abstract_inverted_index.validated | 121 |
| abstract_inverted_index.≤0.050. | 178 |
| abstract_inverted_index.20180916B, | 124 |
| abstract_inverted_index.20190804E, | 162 |
| abstract_inverted_index.20190915D, | 164 |
| abstract_inverted_index.20200223B, | 166 |
| abstract_inverted_index.20201130A, | 168 |
| abstract_inverted_index.20201221B, | 171 |
| abstract_inverted_index.dispersion | 18 |
| abstract_inverted_index.identified | 159 |
| abstract_inverted_index.inactivity | 103 |
| abstract_inverted_index.likelihood | 114 |
| abstract_inverted_index.relatively | 16 |
| abstract_inverted_index.transients | 9 |
| abstract_inverted_index.challenging | 68 |
| abstract_inverted_index.coincidence | 177 |
| abstract_inverted_index.identifying | 37 |
| abstract_inverted_index.mechanisms. | 40 |
| abstract_inverted_index.periodogram | 80, 100, 105 |
| abstract_inverted_index.probability | 174 |
| abstract_inverted_index.successfully | 127 |
| abstract_inverted_index.Additionally, | 179 |
| abstract_inverted_index.characterised | 10 |
| abstract_inverted_index.observations. | 223 |
| abstract_inverted_index.millisecond-scale | 13 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.03435817 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |