Milky Way Satellite Census. IV. Constraints on Decaying Dark Matter from Observations of Milky Way Satellite Galaxies
Bibliographic record
Abstract
Abstract We use a recent census of the Milky Way (MW) satellite galaxy population to constrain the lifetime of particle dark matter (DM). We consider two-body decaying dark matter (DDM) in which a heavy DM particle decays with lifetime τ comparable to the age of the universe to a lighter DM particle (with mass splitting ϵ) and to a dark radiation species. These decays impart a characteristic “kick velocity,” V kick = ϵ c, on the DM daughter particles, significantly depleting the DM content of low-mass subhalos and making them more susceptible to tidal disruption. We fit the suppression of the present-day DDM subhalo mass function (SHMF) as a function of τ and V kick using a suite of high-resolution zoom-in simulations of MW-mass halos, and we validate this model on new DDM simulations of systems specifically chosen to resemble the MW. We implement our DDM SHMF predictions in a forward model that incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk using an empirical model for the galaxy–halo connection. By comparing to the observed MW satellite population, we conservatively exclude DDM models with τ < 18 Gyr (29 Gyr) for V kick = 20 kms−1 (40 kms−1) at 95% confidence. These constraints are among the most stringent and robust small-scale structure limits on the DM particle lifetime and strongly disfavor DDM models that have been proposed to alleviate the Hubble and S 8 tensions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".