Consequences of a dark disk for the Fermi and PAMELA signals in theories with a Sommerfeld enhancement
Bibliographic record
Abstract
Much attention has been given to dark matter explanations of the PAMELA positron fraction and Fermi electronic excesses. For those theories with a TeV-scale WIMP annihilating through a light force-carrier, the associated Sommerfeld enhancement provides a natural explanation of the large boost factor needed to explain the signals, and the light force-carrier naturally gives rise to hard cosmic ray spectra without excess π 0 -gamma rays or anti-protons. The Sommerfeld enhancement of the annihilation rate, which at low relative velocities v rel scales as 1/ v rel , relies on the comparatively low velocity dispersion of the dark matter particles in the smooth halo. Dark matter substructures in which the velocity dispersion is smaller than in the smooth halo have even larger annihilation rates. N-body simulations containing only dark matter predict the existence of such structures, for example subhalos and caustics, and the effects of these substructures on dark matter indirect detection signals have been studied extensively. The addition of baryons into cosmological simulations of disk-dominated galaxies gives rise to an additional substructure component, a dark disk. The disk has a lower velocity dispersion than the spherical halo component by a factor ∼ 6, so the contributions to dark matter signals from the disk can be more significant in Sommerfeld models than for WIMPs without such low-velocity ehancements. We consider the consequences of a dark disk on the observed signals of e + e − , p and γ-rays as measured by Fermi and PAMELA in models where the WIMP annihilations are into a light boson. We find that both the PAMELA and Fermi results are easily accomodated by scenarios in which a disk signal is included with the standard spherical halo signal. If contributions from the dark disk are important, limits from extrapolations to the center of the galaxy contain significant uncertainties beyond those from the spherical halo profile alone.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".