Profile changes associated with dispersion measure events in PSR J1713+0747
Bibliographic record
Abstract
ABSTRACT Propagation effects in the interstellar medium and intrinsic profile changes can cause variability in the timing of pulsars, which limits the accuracy of fundamental science done via pulsar timing. One of the best timing pulsars, PSR J1713+0747, has gone through two ‘dip’ events in its dispersion measure (DM) time series. If these events reflect real changes in electron column density, they should lead to multiple imaging. We show that the events are well fitted by an underdense corrugated sheet model, and look for associated variability in the pulse profile using principal component analysis. We find that there are transient pulse profile variations, but they vary in concert with the DM, unlike what is expected from lensing due to a corrugated sheet. The change is consistent in shape across profiles from both the Green Bank and Arecibo radio observatories, and its amplitude appears to be achromatic across the 820-MHz, 1.4-GHz, and 2.3-GHz bands, again unlike expected from interference between lensed images. This result is puzzling. We note that some of the predicted lensing effects would need higher time and frequency resolution data than used in this analysis. Future events appear likely, and storing baseband data or keeping multiple time–frequency resolutions will allow more in-depth study of propagation effects and hence improvements to pulsar timing accuracy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".