ARECIBO PULSAR SURVEY USING ALFA. III. PRECURSOR SURVEY AND POPULATION SYNTHESIS
Bibliographic record
Abstract
The Pulsar Arecibo L-band Feed Array (PALFA) Survey uses the ALFA 7-beam receiver to search both inner and outer Galactic sectors visible from Arecibo (32° ≲ ℓ ≲ 77° and 168° ≲ ℓ ≲ 214°) close to the Galactic plane (| b | ≲ 5°) for pulsars. The PALFA survey is sensitive to sources fainter and more distant than have previously been seen because of Arecibo's unrivaled sensitivity. In this paper we detail a precursor survey of this region with PALFA, which observed a subset of the full region (slightly more restrictive in ℓ and | b | ≲ 1°) and detected 45 pulsars. Detections included 1 known millisecond pulsar and 11 previously unknown, long-period pulsars. In the surveyed part of the sky that overlaps with the Parkes Multibeam Pulsar Survey (36° ≲ ℓ ≲ 50°), PALFA is probing deeper than the Parkes survey, with four discoveries in this region. For both Galactic millisecond and normal pulsar populations, we compare the survey's detections with simulations to model these populations and, in particular, to estimate the number of observable pulsars in the Galaxy. We place 95% confidence intervals of 82,000 to 143,000 on the number of detectable normal pulsars and 9000 to 100,000 on the number of detectable millisecond pulsars in the Galactic disk. These are consistent with previous estimates. Given the most likely population size in each case (107,000 and 15,000 for normal and millisecond pulsars, respectively), we extend survey detection simulations to predict that, when complete, the full PALFA survey should have detected normal pulsars and millisecond pulsars. Identical estimation techniques predict that normal pulsars and millisecond pulsars would be detected by the beginning of 2014; at the time, the PALFA survey had detected 283 normal pulsars and 31 millisecond pulsars, respectively. We attribute the deficiency in normal pulsar detections predominantly to the radio frequency interference environment at Arecibo and perhaps also scintillation—both effects that are currently not accounted for in population simulation models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.007 | 0.003 |
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".