Pulse profile modelling of the accretion-powered millisecond pulsar SAX J1808.4−3658 using <i>NICER</i> data from its 2019 and 2022 outbursts
Bibliographic record
Abstract
ABSTRACT Pulse profile modelling is a relativistic ray-tracing technique that has provided constraints on parameters, with a focus on mass and radius, of five rotation-powered millisecond pulsars. While the technique can also be applied to accretion-powered millisecond pulsars (AMPs), this requires accounting for the X-rays from the accretion disc and has only been applied to archival data from the Rossi X-ray Timing Explorer. Here, we apply a previously developed neutron star and accretion disc model to the NICER (Neutron star Interior Composition Explorer) data of the 2019 and 2022 outbursts of SAX J1808.4–3658. We find that a single circular hotspot model is insufficient to explain the data. Modelling with two hotspots and an accretion disc model provides better phase-residuals, but a spectral residual at around 1 keV remains. In contrast, we find a good fit with a flexible background approach, replacing the accretion disc. However, the inferred parameters are not robust due to a degeneracy in the origin of the non-pulsed radiation, which can be caused either by the background or a hotspot that is at least partially in view throughout a full rotation. This work represents an important next step in pulse profile modelling of AMPs by analysing NICER data and underlines the need for more accurate accretion disc and hotspot modelling to achieve robust parameter constraints. We expect the inclusion of higher energy and polarimetric data will provide complementary constraints on inclination, hotspot colatitude, and hotspot size, improving the accuracy of pulse profile modelling of AMPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".