Seeking the nearest neutron stars using a new local electron density map
Bibliographic record
Abstract
Abstract Neutron stars provide a compelling testing ground for gravity, nuclear dynamics, and physics beyond the Standard Model, and so it will be useful to locate the neutron stars nearest to Earth. To that end, we revisit pulsar distance estimates extracted from the dispersion measure of pulsar radio waves scattering on electrons. In particular, we create a new electron density map for the local kiloparsec by fitting to parallax measurements of the nearest pulsars, which complements existing maps that are fit on the Galactic scale. This “near-Earth” electron density map implies that pulsars previously estimated to be around a hundred parsecs from Earth will require a long-baseline parallax measurement campaign, both to improve local electron density maps and determine the true distances to these promising pulsars. Nearby neutron stars would be valuable laboratories for testing fundamental physics phenomena, including several late-stage neutron star heating mechanisms, using current and forthcoming telescopes. We estimate the sensitivities of the upcoming Extremely Large Telescope and Thirty Meter Telescope to neutron stars heated by dark matter capture, and find that long observing times are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".