CHIME All-sky Multiday Pulsar Stacking Search (CHAMPSS): System Overview and First Discoveries
Bibliographic record
Abstract
Abstract We describe the Canadian Hydrogen Intensity Mapping Experiment (CHIME) All-sky Multiday Pulsar Stacking Search (CHAMPSS) project. This novel radio pulsar survey revisits the full northern sky daily, offering unprecedented opportunity to detect highly intermittent pulsars, as well as faint sources via long-term data stacking. CHAMPSS uses the CHIME/FRB datastream, which consists of 1024 stationary beams streaming intensity data at 0.983 ms resolution, with 16,384 frequency channels across 400–800 MHz, continuously being searched for single, dispersed bursts/pulses. In CHAMPSS, data from adjacent east–west beams are combined to form a grid of tracking beams, allowing longer exposures at fixed positions. These tracking beams are dedispersed to many trial dispersion measures (DM) to a maximum DM beyond the Milky Way’s expected contribution, and Fourier transformed in time to form power spectra. Repeated observations are searched daily to find intermittent sources, and power spectra of the same sky positions are incoherently stacked, increasing sensitivity to faint persistent sources. The 0.983 ms time resolution limits our sensitivity to millisecond pulsars; we have full sensitivity to pulsars with P > 60 ms, with sensitivity gradually decreasing from 60 ms to 2 ms, as higher harmonics are beyond the Nyquist limit. In a commissioning survey, data covering ∼1/16 of the CHIME sky were processed and searched in quasi-realtime over two months, leading to the discovery of 11 new pulsars, each with S 600 > 0.1 mJy. When operating at scale, CHAMPSS will stack >1 yr of data along each sightline, reaching a sensitivity of ≲30 μJy for all sightlines above a decl. of 10°, and off of the Galactic plane.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".