Reconsidering astrophysical constraints on macroscopic dark matter
Bibliographic record
Abstract
Macroscopic dark matter -- "macros"-- refers to a broad class of alternative candidates to particle dark matter with still unprobed regions of parameter space. These candidates would transfer energy primarily through elastic scattering with approximately their geometric cross-section. For sufficiently large cross-sections, the linear energy deposition could produce observable signals if a macro were to pass through compact objects such as white dwarfs or neutron stars in the form of thermonuclear runaway, leading to a type IA supernova or superburst respectively. We update the constraints from white dwarfs. These are weaker than previously inferred in important respects because of more careful treatment of the passage of a macro through the white dwarf and greater conservatism regarding the size of the region that must be heated to initiate runaway. On the other hand, we place more stringent constraints on macros at low cross-section, using new data from the Montreal White Dwarf Database. New constraints are inferred from the low mass X-ray binary 4U 1820-30, in which more than a decade passed between successive superbursts. Updated microlensing constraints are also reported.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".