A new pulsar timing model for scalar-tensor gravity with applications to PSR J2222-0137 and pulsar-black hole binaries
Bibliographic record
Abstract
Context. Scalar-tensor gravity (STG) theories are well-motivated alternatives to general relativity (GR). One class of STG theories, the Damour-Esposito-Farese (DEF) gravity, has a massless scalar field with two arbitrary coupling parameters. We are interested in this theory because, despite its simplicity, it predicts a wealth of different phenomena, such as dipolar gravitational wave emission and spontaneous scalarization of neutron stars (NSs). These phenomena of DEF gravity can be tested by timing binary radio pulsars. Aims. We aim to develop a new binary pulsar timing model DDSTG to enable more precise tests of STG theories based on a minimal set of binary parameters. The expressions for post-Keplerian (PK) parameters in DEF gravity are self-consistently incorporated into the model. The new technique takes into account all possible correlations between PK parameters naturally. Methods. Grids of physical parameters of NSs are calculated in the framework of DEF gravity for a set of 11 equations of state. The automatic Differentiation (AutoDiff) technique is employed, which aids in the calculation of gravitational form factors of NSs with higher precision than in previous works. The pulsar timing program TEMPO is selected as a framework for the realization of the DDSTG model. The implemented model is applicable to any type of pulsar companions. Results. We apply the DDSTG model to the most recently published observational data for PSR J2222-0137. The obtained limits on DEF gravity parameters for this system confirm and improve previous results. New limits are also the most reliable because DEF gravity is directly fitted to the data. We argue that future observations of PSR J2222-0137 can significantly improve the limits and that PSR-BH systems have the potential to place the tightest limits in certain areas of the DEF gravity parameter space.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".