Constraining the Neutron Star Mass-Radius Relation and Dense Matter Equation of State with NICER. III. Model Description and Verification of Parameter Estimation Codes
Bibliographic record
Abstract
This deposit includes the synthetic NICER data sets along with the necessary auxiliary files used in the cross-verification efforts of the parameter estimation procedures used by Miller et al. (2019) and Riley et al. (2019) to analyze the NICER data of the millisecond pulsar PSR J0030+0451. The parameters assumed in generating the synthetic data are are detailed in the ApJ Letter listed above. Three sets of files are provided corresponding to the following comparison exercises described in the ApJ Letter: 1. Ultra-compact neutron stars with R/M~3 (see Section 2.3 of Letter) -- the tar.gz file and it's MD5 checksum are: multiple_imaging_validation.tar.gz (dd7a02b2ccf0a45da4e9d3a779d3b9c4) 2. A neutron star with a single, uniform-temperature, circular hot spot (see Section 3.1 of Letter) -- the tar.gz file and it's MD5 checksum are: one_spot_synthetic_NICER_data.tar.gz (87b9ca80259b34d76746ef18802a75ec) 3. A neutron star with two different, Uniform-Temperature, circular hot spots (see Section 3.2 of Letter) -- the tar.gz file and it's MD5 checksum are: two_spot_synthetic_NICER_data.tar.gz (75374adc0cea2bc1039f6169548cc719) A readme file contained within each tarball provides detailed information about the file sets used and the parameter values assumed in generating the synthetic data sets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".