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The lensing properties of subhaloes in massive elliptical galaxies in sterile neutrino cosmologies

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We use high-resolution hydrodynamical simulations run with the EAGLE model of galaxy formation to study the differences between the properties of – and subsequently the lensing signal from – subhaloes… Click to show full abstract

We use high-resolution hydrodynamical simulations run with the EAGLE model of galaxy formation to study the differences between the properties of – and subsequently the lensing signal from – subhaloes of massive elliptical galaxies at redshift 0.2, in Cold and Sterile Neutrino (SN) Dark Matter models. We focus on the two 7 keV SN models that bracket the range of matter power spectra compatible with resonantly produced SN as the source of the observed 3.5 keV line. We derive an accurate parametrization for the subhalo mass function in these two SN models relative to cold dark matter (CDM), as well as the subhalo spatial distribution, density profile, and projected number density and the dark matter fraction in subhaloes. We create mock lensing maps from the simulated haloes to study the differences in the lensing signal in the framework of subhalo detection. We find that subhalo convergence is well described by a lognormal distribution and that signal of subhaloes in the power spectrum is lower in SN models with respect to CDM, at a level of 10–80 per cent, depending on the scale. However, the scatter between different projections is large and might make the use of power spectrum studies on the typical scales of current lensing images very difficult. Moreover, in the framework of individual detections through gravitational imaging a sample of ≃30 lenses with an average sensitivity of $M_{\rm {sub}} = 5 \times 10^{7}\, {\rm M}_{\odot}$ would be required to discriminate between CDM and the considered sterile neutrino models.

Keywords: dark matter; sterile neutrino; elliptical galaxies; subhaloes massive; massive elliptical

Journal Title: Monthly Notices of the Royal Astronomical Society
Year Published: 2019

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