Modelling annual scintillation arc variations in PSR J1643−1224 using the Large European Array for Pulsars
Bibliographic record
Abstract
ABSTRACT In this work, we study variations in the parabolic scintillation arcs of the binary millisecond pulsar PSR J1643−1224 over five years using the Large European Array for Pulsars (LEAP). The two-dimensional (2D) power spectrum of scintillation, called the secondary spectrum, often shows a parabolic distribution of power, where the arc curvature encodes the relative velocities and distances of the pulsar, ionized interstellar medium, and Earth. We observe a clear parabolic scintillation arc, which varies in curvature throughout the year. The distribution of power in the secondary spectra is inconsistent with a single scattering screen, which is fully 1D or entirely isotropic. We fit the observed arc curvature variations with two models: an isotropic scattering screen and a model with two independent 1D screens. We measure the distance to the scattering screen to be in the range 114–223 pc, depending on the model, consistent with the known distance of the foreground large-diameter H ii region Sh 2-27 (112 ± 17 pc), suggesting that it is the dominant source of scattering. We obtain only weak constraints on the pulsar’s orbital inclination and longitude of ascending node, since the scintillation pattern is not very sensitive to the pulsar’s motion and the screen is much closer to the Earth than the pulsar. More measurements of this kind – where scattering screens can be associated with foreground objects – will help to inform the origins and distribution of scattering screens within our galaxy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".