[論文レビュー] A systematic search for physical associations between fast radio bursts and astrophysical transients
The paper performs a 3D Bayesian cross-match of 3,765 FRBs with 701 GRBs, 51,054 optical transients, and 17 X-ray transients to search for physically associated FRB-AT pairs, finding no new statistically significant associations beyond a known FRB 20180916B–AT2020hur pair.
The physical origin of fast radio bursts (FRBs) remains an unsolved mystery in astrophysics, with the magnetar central engine model as the leading framework. Systematically searching for physical associations between FRBs and the energetic astrophysical transients (ATs) that form magnetars provides a critical test of this scenario, and key clues to FRB progenitors. We perform a systematic search for FRB-AT associations using a sample of 3765 unique FRBs, combining the second CHIME/FRB catalog with 124 additional localized FRBs with measured redshifts. We develop a 3D Bayesian inference framework that jointly incorporates angular separation, positional uncertainty, and redshift constraints to quantify the association probability of candidate pairs. Through spatial cross-matching, we identify 14 FRB-optical transient and 15 FRB-gamma-ray burst (GRB) candidate pairs. Our framework recovers the previously reported high-significance association between FRB 20180916B and AT 2020hur, with an association probability of 0.9998. For the proposed candidate FRB 20190309A and short GRB 060502B, our analysis yields an association probability of 0.83, which is insufficient to claim statistically significant association. No new statistically significant FRB-AT associations are found for all remaining candidates. Our work demonstrates that small angular separation alone is insufficient to confirm FRB-AT associations, and high-precision FRB localization is essential for definitive identification.
研究の動機と目的
- Test whether FRBs are physically associated with magnetar-producing transients (GRBs, SNe, etc.) by cross-matching large FRB and AT samples.
- Develop a 3D Bayesian framework that combines angular separations, positional uncertainties, and redshift information to quantify association probabilities.
- Assess previously proposed FRB-AT associations in a statistically robust, catalog-wide context.
- Demonstrate that precise FRB localization is crucial for establishing genuine FRB-AT associations.
提案手法
- Construct a joint 3D Bayesian framework that uses angular distance, positional errors, and redshift distributions to compute association probabilities.
- Model FRB redshifts using a DM_E-based posterior p(z|DM_E) with IGM and host DM contributions, calibrated from localized FRBs.
- Define two hypotheses H1 (physical association) and H0 (chance alignment), deriving likelihoods for angular and redshift data under each hypothesis.
- Use a conservative prior P(H1)=π1 with π1≈1/(N_FRB N_AT) to suppress look-elsewhere false positives.
- Compute Bayes factor B from p(θ,z_AT|H1) and p(θ,z_AT|H0) and obtain P_asso as the posterior association probability via Bayes' theorem.
- Perform spatial cross-matching with θ criteria (FRB-OT: θ<1 arcmin; FRB-GRB: within FRB error regions) before applying the Bayesian framework.
実験結果
リサーチクエスチョン
- RQ1Can FRBs be statistically associated with energetic transients that form magnetars (GRBs, SNe, etc.) after accounting for angular, redshift, and survey-selection effects?
- RQ2Does high-precision FRB localization enable robust identification of FRB-AT associations beyond chance coincidences?
- RQ3Do previously claimed FRB-AT associations hold under a rigorous 3D Bayesian analysis with larger FRB samples?
主な発見
| FRBs | RA_FRB | Dec_FRB | DM_E | OT/GRB/XT | RA_OT/GRB/XT | Dec_OT/GRB/XT | theta (deg) | z_OT/z_GRB | Bayes Factor | sigma | P_asso |
|---|---|---|---|---|---|---|---|---|---|---|---|
| FRB 20180916B | 29.5031258 | 65.716754 | 101.0481 | AT2020hur 1 | 29.503125 | 65.71675 | 4.23e-06 | -- | 2.78e-4 | ~1e10 | 99.98% |
| FRB 20221025G | 13.286 | 53.995 | 1046.1817 | SN2024adfq | 13.310 | 53.994 | 0.014 | 0.2285 | ~1e5 | 0.082 | ~1e-3 |
| FRB 20220224A | 43.438 | 13.006 | 254.9588 | SN2025pfh 1 | 43.426 | 13.016 | 0.015 | 0.012142 | ~1e5 | 0.077 | ~1e-3 |
| FRB 20230614C | 245.838 | 37.266 | 109.2904 | PS15boe 1 | 245.841 | 37.253 | 0.013 | 0.033843 | ~1e5 | 0.209 | ~1e-4 |
| FRB 20220424D | 188.675 | 62.330 | 3117.1339 | AT2025mbm 1 | 188.672 | 62.333 | 0.003 | 0.135823 | ~1e4 | 0.112 | ~1e-4 |
| FRB 20190317D | 238.092 | 47.049 | 1034.0295 | AT2019pmq 1 | 238.100 | 47.061 | 0.013 | 0.162 | ~1e4 | 0.138 | ~1e-4 |
| FRB 20221019B | 302.684 | 86.489 | 1277.2989 | SN2022foj | 302.747 | 86.493 | 0.006 | 0.058 | ~1e4 | 0.038 | ~1e-5 |
| FRB 20190502A | 164.958 | 59.947 | 540.8446 | SN2020adii | 164.973 | 59.954 | 0.010 | 0.045077 | ~1e4 | 0.102 | ~1e-5 |
| FRB 20200813C | 286.265 | 34.390 | 318.2156 | SN2021soe | 286.251 | 34.392 | 0.012 | 0.012 | ~1e4 | 0.219 | ~1e-5 |
| FRB 20210408A | 22.547 | 19.157 | 431.5286 | AT2020acvc 1 | 22.536 | 19.169 | 0.016 | 0.045438 | ~1e4 | 0.131 | ~1e-5 |
| FRB 20210604B | 164.883 | 47.487 | 559.4512 | AT2023jk 1 | 164.898 | 47.499 | 0.016 | 0.100061 | ~1e4 | 0.172 | ~1e-5 |
| FRB 20250316A | 182.435 | 58.849 | 79.52 | SN2008X | 182.451 | 58.850 | 0.008 | 0.0063 | ~0 | 3.55e-4 | ~0 |
| FRB 20250316A | 182.435 | 58.849 | 79.52 | SN2009E | 182.457 | 58.847 | 0.012 | 0.0063 | ~0 | 3.55e-4 | ~0 |
| FRB 20250316A | 182.435 | 58.849 | 79.52 | AT2025erx 1 | 182.461 | 58.841 | 0.016 | 0.006354 | ~0 | 3.55e-4 | ~0 |
| FRB 20190309A | 278.947 | 52.407 | 248.2137 | SGRB 060502B | 278.938 | 52.633 | 0.226 | 0.287 | ~1e6 | 0.204 | 83.25% |
| FRB 20210917A | 184.026 | 20.287 | 1005.4806 | GRB 140318A | 184.054 | 20.200 | 0.091 | 1.02 | ~1e5 | 0.256 | 10.70% |
| FRB 20200721E | 69.174 | 85.338 | 530.3554 | GRB 211023A | 72.300 | 85.300 | 0.258 | 0.39 | ~1e5 | 0.213 | 9.92% |
| FRB 20191103B | 234.230 | 16.145 | 806.8435 | GRB 150518A | 234.208 | 16.267 | 0.123 | 0.256 | ~1e4 | 1.021 | ~1e-3 |
| FRB 20200314H | 107.061 | 27.079 | 981.6112 | GRB 160228A | 107.321 | 26.950 | 0.265 | 1.64 | ~1e3 | 0.246 | ~1e-3 |
| FRB 20181022F | 179.227 | 17.111 | 647.5824 | GRB 250321D | 179.263 | 17.383 | 0.275 | 4.368 | ~0 | 0.251 | ~0 |
| FRB 20190429A | 281.038 | 59.424 | 363.2501 | GRB 140713A | 281.113 | 59.617 | 0.197 | 0.935 | ~0 | 0.198 | ~0 |
| FRB 20200705D | 187.378 | 47.879 | 289.0007 | GRB 250920B | 187.338 | 47.850 | 0.040 | 2.2 | ~0 | 0.171 | ~0 |
| FRB 20201026F | 14.285 | -5.724 | 734.3873 | GRB 251002A | 13.904 | -5.550 | 0.417 | 2.178 | ~0 | 0.349 | ~0 |
| FRB 20210504C | 238.095 | 78.505 | 625.6949 | GRB 060510B | 239.142 | 78.567 | 0.217 | 4.94 | ~0 | 0.178 | ~0 |
| FRB 20210603B | 122.846 | 21.986 | 636.1292 | GRB 141121A | 122.667 | 22.233 | 0.298 | 1.47 | ~0 | 0.245 | ~0 |
| FRB 20211201B | 206.429 | 43.997 | 369.4879 | GRB 080319A | 206.354 | 44.083 | 0.102 | 2.0265 | ~0 | 0.215 | ~0 |
| FRB 20220426D | 195.064 | 32.127 | 118.083 | GRB 141220A | 195.050 | 32.150 | 0.026 | 1.3195 | ~0 | 0.222 | ~0 |
| FRB 20230508C | 324.113 | 6.645 | 471.4404 | GRB 061110B | 323.900 | 6.883 | 0.318 | 3.44 | ~0 | 0.234 | ~0 |
| FRB 20230915C | 1.326 | 31.878 | 748.879 | GRB 220101A | 1.379 | 31.750 | 0.136 | 4.618 | ~0 | 0.222 | ~0 |
- No new statistically significant FRB-AT associations were found beyond known pairs.
- FRB 20180916B and AT 2020hur yields a high association probability of 0.9998 using positional data alone.
- FRB 20190309A–GRB 060502B has an association probability of 0.83, not sufficient for a significant claim.
- Most other FRB-OT and FRB-GRB candidate pairs have association probabilities near zero under the framework.
- The analysis confirms that small angular separation alone is insufficient for confirmation and that precise FRB localization is essential.
- Reproduces earlier high-significance result for FRB 20180916B–AT2020hur and reconciles the FRB 20190309A–GRB 060502B result with a larger FRB sample.
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