High-precision X-Ray Timing of Three Millisecond Pulsars with NICER: Stability Estimates and Comparison with Radio
Bibliographic record
Abstract
Abstract The Neutron Star Interior Composition Explorer (NICER) is an X-ray astrophysics payload on the International Space Station. It enables unprecedented high-precision timing of millisecond pulsars (MSPs) without the pulse broadening and delays due to dispersion and scattering within the interstellar medium that plague radio timing. We present initial timing results from a year of data on the MSPs PSR B1937+21 and PSR J0218+4232, and nine months of data on PSR B1821−24. NICER time-of-arrival uncertainties for the three pulsars are consistent with theoretical lower bounds and simulations based on their pulse shape templates and average source and background photon count rates. To estimate timing stability, we use the σ z measure, which is based on the average of the cubic coefficients of polynomial fits to subsets of timing residuals. So far we are achieving timing stabilities σ z ≈ 3 × 10−14 for PSR B1937+21 and on the order of 10−12 for PSRs B1821−24 and J0218+4232. Within the span of our NICER data we do not yet see the characteristic break point in the slope of σ z ; detection of such a break would indicate that further improvement in the cumulative root-mean-square timing residual is limited by timing noise. We see this break point in our comparison radio data sets for PSR B1821−24 and PSR B1937+21 on timescales of >2 yr.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".