Arecibo Pulsar Survey Using ALFA. I. Survey Strategy and First Discoveries
Bibliographic record
Abstract
We report results from the initial stage of a long-term pulsar survey of the Galactic plane using the Arecibo L-band Feed Array (ALFA), a seven-beam receiver operating at 1.4 GHz with 0.3 GHz bandwidth, and fast-dump digital spectrometers. The search targets low Galactic latitudes, | b | ≲ 5°, in the accessible longitude ranges 32° ≲ ℓ ≲ 77° and 168° ≲ ℓ ≲ 214°. The instrumentation, data processing, initial survey observations, sensitivity, and database management are described. Data discussed here were collected over a 100 MHz passband centered on 1.42 GHz using a spectrometer that recorded 256 channels every 64 μs. Analysis of the data with their full time and frequency resolutions is ongoing. Here we report the results of a preliminary, low-resolution analysis for which the data were decimated to speed up the processing. We have detected 29 previously known pulsars and discovered 11 new ones. One of these, PSR J1928+1746, with a period of 69 ms and a relatively low characteristic age of 82 kyr, is a plausible candidate for association with the unidentified EGRET source 3EG J1928+1733. Another, PSR J1906+07, is a nonrecycled pulsar in a relativistic binary with an orbital period of 3.98 hr. In parallel with the periodicity analysis, we also search the data for isolated dispersed pulses. This technique has resulted in the discovery of PSR J0628+09, an extremely sporadic radio emitter with a spin period of 1.2 s. Simulations we have carried out indicate that ~1000 new pulsars will be found in our ALFA survey. In addition to providing a large sample for use in population analyses and for probing the magnetoionic interstellar medium, the survey maximizes the chances of finding rapidly spinning millisecond pulsars and pulsars in compact binary systems. Our search algorithms exploit the multiple data streams from ALFA to discriminate between radio frequency interference and celestial signals, including pulsars and possibly new classes of transient radio sources.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".