Probing the Diversity of Fast Radio Bursts with CHIME/FRB
Bibliographic record
Abstract
Fast radio bursts (FRBs) are extremely bright extragalactic radio transients that flash for microseconds to milliseconds at a time, most never to repeat again. Encoded in every observed FRB is information from burst propagation effects, giving us clues about their mysterious origins as well as the environments they traveled through. With inferred all-sky rates of hundreds per day, FRBs have held great interest for those interested in extreme astrophysical processes as well as those interested in cosmological properties of the Universe. The Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB project has revolutionized the FRB field with its field-leading discovery rate. With CHIME/FRB, we can start to carry out population-level studies of FRBs to constrain their origins and inform their use as cosmological probes. I present the first population-level studies of CHIME/FRB-observed FRBs using the CHIME/FRB Catalog 1 data release and the injections system to account for observational biases. I discover that CHIME/FRB is likely observationally biased against bursts originating from turbulent local environments, and constrain the energy and distance distributions of FRBs. I also present the Catalog 1 dataset updated with channelized raw voltage (“baseband”) data (“BaseCat1”), for which I played a pivotal role. The CHIME/FRB baseband localization pipeline can localize FRBs to arcminute-precision as long as the signal is bright enough to trigger the saving of offline baseband data. I then discuss two single source-studies enabled by the baseband localization pipeline — one discovering repeaters during phases of unusually heightened burst activity, and one using the burst properties of an unusual FRB to probe the properties of its sightline. In the latter study, I constrain the electron density content of a diffuse filamentary structure on the outskirts of the Virgo Cluster, demonstrating the power of FRBs as probes of diffuse media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".