Professor Pedro Ferreira

Abstract:

Ultralight axions (ULAs) with masses in the range 10-33 eV ma 10-18 eV (motivated by string theory) might contribute to the dark-matter or dark-energy density of the Universe. ULAs would suppress the growth of structure on small scales and change the shape of the cosmic microwave background (CMB) anisotropy power spectra. In this work, we compute cosmological observables over the full ULA mass range and then use them to search for evidence of ULAs using CMB temperature data from the Planck satellite, large-scale CMB polarization data from Wilkinson Microwave Anisotropy Probe (WMAP), smallerscale CMB experiments, as well as the WiggleZ galaxy-redshift survey. In the mass range 10-32 eV ma 10-25.5 eV, the ULA relic-density must obey the constraint ah2 0.006 at 95%-confidence. For ma & 10-24 eV, ULAs are indistinguishable from standard cold dark matter on the length scales probed while for ma . 10-32 eV, ULAs are allowed to compose a significant fraction of the dark energy. If primordial gravitational waves are detected, limits to the primordial isocurvature fraction will put severe constraints on ULA dark matter. In the future, weak-lensing measurements of the CMB will yield even more powerful probes of the ULA hypothesis.

Abstract:

We make use of a large set of fast simulations of an intensity mapping experiment with characteristics similar to those expected of the Square Kilometre Array in order to study the viability and limits of blind foreground subtraction techniques. In particular, we consider three different approaches: polynomial fitting, principal component analysis (PCA) and independent component analysis (ICA). We review the motivations and algorithms for the three methods, and show that they can all be described, using the same mathematical framework, as different approaches to the blind source separation problem. We study the efficiency of foreground subtraction both in the angular and radial (frequency) directions, as well as the dependence of this efficiency on different instrumental and modelling parameters. For well-behaved foregrounds and instrumental effects, we find that foreground subtraction can be successful to a reasonable level on most scales of interest. We also quantify the effect that the cleaning has on the recovered signal and power spectra. Interestingly, we find that the three methods yield quantitatively similar results, with PCA and ICA being almost equivalent.