The Implementation of a Fast-folding Pipeline for Long-period Pulsar Searching in the PALFA Survey
Bibliographic record
Abstract
Abstract The Pulsar Arecibo L-Band Feed Array (PALFA) survey, the most sensitive blind search for radio pulsars yet conducted, is ongoing at the Arecibo Observatory in Puerto Rico. The vast majority of the 180 pulsars discovered by PALFA have spin periods shorter than 2 s. Pulsar surveys may miss long-period radio pulsars owing to the summing of a finite number of harmonic components in conventional Fourier analyses (typically ∼16), or as a result of the strong effect of red noise at low modulation frequencies. We address this reduction in sensitivity by using a time-domain search technique: the fast-folding algorithm (FFA). We designed a program that implements an FFA-based search in the PALFA processing pipeline and tested the efficiency of the algorithm by performing tests under both ideal, white-noise conditions, as well as with real PALFA observational data. In the two scenarios, we show that the time-domain algorithm has the ability to outperform the FFT-based periodicity search implemented in the survey. We perform simulations to compare the previously reported PALFA sensitivity with that obtained using our new FFA implementation. These simulations show that for a pulsar having a pulse duty cycle of roughly 3%, the performance of our FFA pipeline exceeds that of our FFT pipeline for pulses with dispersion measure ≲ 40 pc cm−3 and for periods as short as ∼500 ms, and that the survey sensitivity is improved by at least a factor of two for periods ≳ 6 s. Early results from the implementation of the algorithm in PALFA, including discoveries, are also presented in this paper.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".