Velocity-dependent self-interacting dark matter from groups and clusters of galaxies
Bibliographic record
Abstract
We probe the self-interactions of dark matter with relaxed galaxy groups and clusters using observational data from strong and weak lensing and stellar kinematics. Our analysis uses the Jeans formalism and considers a wider range of systematic effects than in previous work, including adiabatic contraction and stellar anisotropy, to robustly constrain the self-interaction cross section. For both groups and clusters, our results show a mild preference for a nonzero cross section compared with cold collisionless dark matter. Our groups result, σ/m=0.5±0.2 cm2/g, places the first constraint on self-interacting dark matter (SIDM) at an intermediate scale M200∼ 1014 M⊙ between galaxies and massive clusters. For massive clusters with M200∼ 1015 M⊙, our result is σ/m=0.19±0.09 cm2/g, with an upper limit of σ / m < 0.35 cm2/g (95% CL) . Thus, our results disfavor a velocity-independent cross section of order 1 cm2/g or larger needed to impact small scale structure problems in galaxies, but are consistent with a velocity-dependent cross section that decreases with increasing scattering velocity. Comparing the cross sections with and without the effect of adiabatic contraction, we find that adiabatic contraction produces slightly larger values for our data sample, but they are consistent at the 1σ level. Finally, to validate our approach, we apply our Jeans analysis to a sample of mock data generated from SIDM-plus-baryons simulations with σ/m = 1 cm2/g. This is the first test of the Jeans model at the level of stellar and lensing observables directly measured from simulations. We find our analysis gives a robust determination of the cross section, as well as consistently inferring the true baryon and dark matter density profiles.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".