High-time resolution search for compact objects using fast radio burst gravitational lens interferometry with CHIME/FRB
Bibliographic record
Abstract
The gravitational field of compact objects, such as primordial black holes, can create multiple images of background sources. For transients such as fast radio bursts (FRBs), these multiple images can be resolved in the time domain. Under certain circumstances, these images not only have similar burst morphologies but are also phase-coherent at the electric field level. With a novel dechannelization algorithm and a matched filtering technique, we search for repeated copies of the same electric field waveform in observations of FRBs detected by the FRB backend of the Canadian Hydrogen Mapping Intensity Experiment (CHIME). An interference fringe from a coherent gravitational lensing signal will appear in the time-lag domain as a statistically significant peak in the time-lag autocorrelation function. We calibrate our statistical significance using telescope data containing no FRB signal. Our dataset consists of $\ensuremath{\sim}100$-ms long recordings of voltage data from 172 FRB events, dechannelized to 1.25-ns time resolution. This coherent search algorithm allows us to search for gravitational lensing signatures from compact objects in the mass range of ${10}^{\ensuremath{-}4}--{10}^{4}\text{ }\text{ }{\mathrm{M}}_{\ensuremath{\bigodot}}$. After ruling out an anomalous candidate due to diffractive scintillation, we find no significant detections of gravitational lensing in the 172 FRB events that have been analyzed. In a companion work [C. Leung and Z. Kader, Phys. Rev. D 106, 043017 (2022).], we interpret the constraints on dark matter from this search.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".