The Intrinsic Scatter of the Radial Acceleration Relation*
Bibliographic record
Abstract
Abstract We present a detailed Monte Carlo model of observational errors in observed galaxy scaling relations to recover the intrinsic (cosmic) scatter driven by galaxy formation and evolution processes. We apply our method to the stellar radial acceleration relation (RAR), which compares the local observed radial acceleration to the local Newtonian radial acceleration computed from the stellar mass distribution. The stellar and baryonic RAR are known to exhibit similar scatter. Lelli+2017 (L17) studied the baryonic RAR using a sample of 153 spiral galaxies and inferred a negligible intrinsic scatter. If true, a small scatter might challenge the ΛCDM galaxy formation paradigm, possibly favoring a modified Newtonian dynamics interpretation. The intrinsic scatter of the baryonic RAR is predicted by modern ΛCDM simulations to be ∼0.06–0.08 dex, contrasting with the null value reported by L17. We have assembled a catalog of structural properties with over 2500 spiral galaxies from six deep imaging and spectroscopic surveys (called the “Photometry and Rotation curve OBservations from Extragalactic Surveys”) to quantify the intrinsic scatter of the stellar RAR and other scaling relations. The stellar RAR for our full sample has a median observed scatter of 0.17 dex. We use our Monte Carlo method, which accounts for all major sources of measurement uncertainty, to infer a contribution of 0.12 dex from the observational errors. The intrinsic scatter of the stellar RAR is thus estimated to be 0.11 ± 0.02 dex, in agreement with, though slightly greater than, current ΛCDM predictions.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".