A VLBI Software Correlator for Fast Radio Transients
Bibliographic record
Abstract
Abstract One major goal in fast radio burst science is to detect fast radio bursts (FRBs) over a wide field of view without sacrificing the angular resolution required to pinpoint them to their host galaxies. Wide-field detection and localization capabilities have already been demonstrated using connected-element interferometry; the CHIME/FRB Outriggers project will push this further using widefield cylindrical telescopes as widefield outriggers for very long baseline interferometry (VLBI). This paper describes an offline VLBI software correlator written in Python for the CHIME/FRB Outriggers project. It includes features well-suited to modern widefield instruments like multibeaming/multiple phase center correlation, pulse gating including coherent dedispersion, and a novel correlation algorithm based on the quadratic estimator formalism. This algorithm mitigates sensitivity loss that arises in instruments where the windowing and channelization is done outside the VLBI correlator at each station, which accounts for a 30% sensitivity drop away from the phase center. Our correlation algorithm recovers this sensitivity on both simulated and real data. As an end-to-end check of our software, we have written a preliminary pipeline for VLBI calibration and single-pulse localization, which we use in Lanman et al. to verify the astrometric accuracy of the CHIME/FRB Outriggers array.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".