Shedding light on low-mass subhalo survival and annihilation luminosity with numerical simulations
Bibliographic record
Abstract
ABSTRACT In this work, we carry out a suite of specially designed numerical simulations to shed light on dark matter (DM) subhalo survival at mass scales relevant for gamma-ray DM searches, a topic subject to intense debate nowadays. We have employed an improved version of DASH, a GPU N-body code, to study the evolution of low-mass subhaloes inside a Milky-Way-like halo with unprecedented accuracy, reaching solar-mass and sub-parsec resolution. We simulate subhaloes with varying mass, concentration, and orbital properties, and consider the effect of baryons in the host. We analyse the evolution of the bound mass fraction and annihilation luminosity, finding that most subhaloes survive until present, yet losing in some cases more than 99 per cent of their initial mass. Baryons induce a much greater mass-loss, especially when the subhalo orbit is more parallel to the Galactic disc. Many of these subhaloes cross the solar Galactocentric radius, making it easier to detect their annihilation fluxes from Earth. We find subhaloes orbiting a DM-only halo with a pericentre in the solar vicinity to lose 70–90 per cent of their initial annihilation luminosity at present, which increases up to 99 per cent when including baryons. We find a strong relation between subhalo’s mass-loss and the effective tidal field at pericentre. Indeed, much of the dependence on all considered parameters can be explained through this single parameter. In addition to shedding light on the survival of low-mass Galactic subhaloes, our results can provide detailed predictions that will aid current and future quests for the nature of DM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".