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Gravitinos with a mass in the keV range are an interesting candidate for warm dark matter. Recent measurements of the matter density of the universe and of cosmic structures at the dwarf galaxy scale rule out the simplest gravitino models with thermal freeze-out. We construct a model where the decay of the messenger particles that transmit the supersymmetry breaking to the observable sector generates the required entropy to dilute the gravitino relic density by the required factor of a few to come in line with observations. The model is natural, and requires only that the coupling of the messenger sector to the standard model be set so that the decay happens at the appropriate time.
8 pages, one figure
72, Standard Model, Nonluminous Matter, Universe Supersymmetry Phenomenology, Entropy, Astrophysics (astro-ph), Production, FOS: Physical sciences, Decay, Astrophysics, 530, High Energy Physics - Phenomenology, Kev Range, High Energy Physics - Phenomenology (hep-ph), Supersymmetry Phenomenology, Dark Matter, Supersymmetry
72, Standard Model, Nonluminous Matter, Universe Supersymmetry Phenomenology, Entropy, Astrophysics (astro-ph), Production, FOS: Physical sciences, Decay, Astrophysics, 530, High Energy Physics - Phenomenology, Kev Range, High Energy Physics - Phenomenology (hep-ph), Supersymmetry Phenomenology, Dark Matter, Supersymmetry
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 65 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |