We present a method to detect non-Gaussianity in CMB temperature fluctuations maps, based on the spherical Mexican Hat wavelet. We have applied this method to artificially generated non-Gaussian maps using the Edgeworth expansion. Analysing the skewness and kurtosis of the wavelet coefficients in contrast to Gaussian simulations, the Mexican Hat is more efficient in detecting non-Gaussianity than the spherical Haar wavelet for all different leves of non-Gaussianity introduced. These results are relevant to test the Gaussian character of the CMB data. The method has also been applied to non-Gaussian maps generated by introducing an additional quadratic term in the gravitational potential.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.