4.7 Article

Isotropy analyses of the Planck convergence map

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 473, Issue 1, Pages 165-172

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stx2240

Keywords

gravitational lensing: weak; cosmic background radiation

Funding

  1. CAPES
  2. CNPq
  3. CAPES PVE [88881.064966/2014-01]
  4. DTI-PCI Programme of the Brazilian Ministry of Science, Technology and Innovation (MCTI)
  5. CNPq's grant PDE [234529/2014-08]

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The presence of matter in the path of relic photons causes distortions in the angular pattern of the cosmic microwave background (CMB) temperature fluctuations, modifying their properties in a slight but measurable way. Recently, the Planck Collaboration released the estimated convergence map, an integrated measure of the large-scale matter distribution that produced the weak gravitational lensing (WL) phenomenon observed in Planck CMB data. We perform exhaustive analyses of this convergence map calculating the variance in small and large regions of the sky, but excluding the area masked due to Galactic contaminations, and compare them with the features expected in the set of simulated convergence maps, also released by the Planck Collaboration. Our goal is to search for sky directions or regions where the WL imprints anomalous signatures to the variance estimator revealed through a chi(2) analyses at a statistically significant level. In the local analysis of the Planck convergence map, we identified eight patches of the sky in disagreement, in more than 2 sigma, with what is observed in the average of the simulations. In contrast, in the large regions analysis we found no statistically significant discrepancies, but, interestingly, the regions with the highest chi(2) values are surrounding the ecliptic poles. Thus, our results show a good agreement with the features expected by the Lambda cold dark matter concordance model, as given by the simulations. Yet, the outliers regions found here could suggest that the data still contain residual contamination, like noise, due to over- or underestimation of systematic effects in the simulation data set.

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