Localization and Lensing of Fast Radio Bursts using CHIME/FRB and its VLBI Outriggers
Bibliographic record
Abstract
Every two minutes, a luminous, millisecond-duration flash of radio light arrives at Earth from outside the Milky Way. These elusive fast radio bursts (FRBs) last just a millisecond, and the vast majority are never detected again. FRBs are powerful probes of dark matter and cosmological structure, and offer insights into magnetars: a rare class of neutron stars which produce the strongest magnetic fields in the Universe. However, because FRBs are so fleeting, the field is grappling with much simpler questions: How do magnetars emit FRBs? From what galaxies (and redshifts) do FRBs originate? Pinpointing FRBs to their host galaxies using the Canadian Hydrogen Intensity Mapping Experiment (CHIME) is perhaps the single most promising path towards uncovering the mystery of FRBs. CHIME detects about 700 FRBs per year, but lacks the resolution to pinpoint its bursts. Very-long baseline interferometry (VLBI) is a solution which uses widely-separated telescopes to achieve high angular resolution, but this technique has been limited to following up the small fraction of sources which repeat. In this thesis, I develop key technologies to combine wide-field observations for FRB detection with high angular resolution for FRB localization in one instrument, including high-bandwidth digital instrumentation, a stable reference clock for CHIME, and two telescopes, observing in tandem with CHIME over 3000-kilometer baselines. I wrote a VLBI correlator to analyze data from the testbeds, and used the array to successfully pinpoint a one-off FRB with sub-arcsecond precision at the time of detection. This sets the stage for CHIME Outriggers: three dedicated telescopes which will enhance CHIME’s angular resolution to sub-arcsecond scales over CHIME’s entire field of view, pushing FRB science into an era of plentiful and precise localizations. I also develop a new way to use FRBs as probes of sub-solar mass primordial black holes. By exploiting multi-path interference in gravitational lensing, I conducted a novel search for lensed FRBs. We find that some FRBs exhibit plasma lensing (scintillation), which we attribute to the Milky Way’s interstellar medium, and use our null search to place new constraints on extragalactic primordial black holes as dark matter.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".