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Dark matter searches with Cherenkov telescopes: nearby dwarf galaxies or local galaxy clusters?

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 تاريخ النشر 2011
  مجال البحث فيزياء
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In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC5813 and NGC5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for IACTs [ABRIDGED]



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