Analysis of Multi-hour Continuous Observations of Seven Millisecond Pulsars
Bibliographic record
Abstract
Abstract Precision pulsar timing can be used for a variety of astrophysical tests, from the detection of gravitational waves to probing the properties of the interstellar medium. Here we analyze various noise contributions to pulsar timing residuals from continuous multi-hour observations of seven millisecond pulsars (MSPs). We present scintillation bandwidth measurements for all MSPs in the sample, some for the first time. We also present scintillation timescale measurements and lower limits for all MSPs for the first time. In addition, we present upper limits on the contribution of pulse phase jitter to the timing residual error for all MSPs. These long observations also allow us to constrain variations in dispersion measures (DMs) on hour-long timescales for several millisecond pulsars. We find that there are no apparent DM variations in any of the MSPs studied on these timescales, as expected. In light of new radio telescopes, such as the Canadian Hydrogen Intensity Mapping Experiment, which will be able to time many pulsars for a short time each day, we search for differences in timing precisions from continuous pulse times of arrival (TOAs) and from equivalent length time-discontinuous TOAs. We find no differences in the precision for any of the MSPs in our sample, as expected. We conclude that the TOA variations are consistent with the expected breakdown into template-fitting, jitter, and scintillation errors.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".