Controlling for the Ionospheric and Baseline-Offset Uncertainties in the CHIME/FRB Outriggers VLBI Network for Milliarcsecond Precision
Bibliographic record
Abstract
Fast radio bursts (FRBs) are a novel form of radio transients discovered in 2007. These bright, extragalactic radio signals have an inferred all-sky rate of hundreds of detections per day. The properties of FRBs hold valuable clues about the extreme physical processes driving them while also holding information about the astrophysical plasmas they traverse on their journey to Earth. The Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB project has led the field with the hundreds of FRB detections the collaboration has published to date. However, these detections typically have localization regions so large that we cannot identify a single host galaxy, never mind its local environment. To improve upon this, CHIME/FRB has been transformed into a very long baseline interferometry (VLBI) array, drastically increasing the angular resolution of CHIME/FRB from arcminute to sub-arcsecond precision. In this work, I present my contributions to commissioning the CHIME/FRB VLBI Outrigger station located at the Green Bank Observatory (GBO) in West Virginia. This includes measuring and validating GBO's exact position to enable the localization of FRBs to sub-arcsecond precision. For VLBI networks spanning thousands of kilometers, the difference in the local ionospheric environments is significant and leads to errors in the CHIME/FRB Outrigger localizations. I present a thin shell model of the ionosphere to parameterize the local ionospheric environment for each VLBI station. This model may be used to interpolate the error induced by the ionosphere in FRB observations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".