PALFA Single-pulse Pipeline: New Pulsars, Rotating Radio Transients, and a Candidate Fast Radio Burst
Bibliographic record
Abstract
Abstract We present a new single-pulse pipeline for the PALFA survey to efficiently identify single radio pulses from pulsars, rotating radio transients (RRATs), and fast radio bursts (FRBs). We conducted a sensitivity analysis of this new pipeline in which many single pulses were injected into PALFA data and run through the pipeline. We find that for single pulse widths <5 ms, the sensitivity of our new pipeline is at most a factor of ∼2 less sensitive than theoretically predicted. For pulse widths >10 ms, as the DM decreases, the degradation in sensitivity gets worse and can increase up to a factor of ∼4.5. Using this pipeline, we have discovered seven pulsars and two RRATs, and identified three candidate RRATs and one candidate FRB. The confirmed pulsars and RRATs have DMs ranging from 133 to 386 pc cm−3 and flux densities ranging from 20 to 160 mJy. The pulsar periods range from 0.4 to 2.1 s. We report on candidate FRB 141113, which is likely astrophysical and extragalactic, having DM ≃ 400 pc cm−3, which is over the Galactic maximum along this line of sight by ∼100–200 pc cm−3. We consider implications for the FRB population and show via simulations that if FRB 141113 is real and extragalactic, the slope α of the distribution of integral source counts as a function of flux density (N(>S) ∝ S −α ) is 1.4 ± 0.5 (95% confidence range). However, this conclusion is dependent on assumptions that require verification.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".