Comparing dark matter and MOND hyphotheses from the distribution function of A, F, early-G stars in the solar neighbourhood
Bibliographic record
Abstract
Dark matter is hypothetical matter believed to address the missing mass problem in galaxies. However, alternative theories, such as Modified Newtonian Dynamics (MOND), have been notably successful in explaining the missing mass problem in various astrophysical systems. The vertical distribution function of stars in the solar neighbourhood serves as a proxy to constrain galactic dynamics in accordance to its contents. We employ both the vertical positional and velocity distribution of stars in cylindrical coordinates with a radius of 150 pc and a half-height of 200 pc from the galactic plane. Our tracers consist of main-sequence A, F, and early-G stars from the GAIA, RAVE, APOGEE, GALAH, and LAMOST catalogues. We attempt to solve the missing mass in the solar neighbourhood, interpreting it as either dark matter or MOND. Subsequently, we compare both hypotheses newtonian gravity with dark matter and MOND, using the Bayes factor (BF) to determine which one is more favoured by the data. We found that the inferred dark matter in the solar neighbourhood is in range of $\sim (0.01$-$0.07)$ M$_{\odot}$ pc$^{-3}$. We also determine that the MOND hypothesis's acceleration parameter $a_0$ is $(1.26 \pm 0.13) \times 10^{-10}$ m s$^{-2}$ for simple interpolating function. The average of bayes factor for all tracers between the two hypotheses is $\log \textrm{BF}\sim 0.1$, meaning no strong evidence in favour of either the dark matter or MOND hypotheses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".