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Cited article:

Advances in Machine Learning and Data Mining for Astronomy

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Effect of component separation on the temperature distribution of the cosmic microwave background

R. B. Barreiro, E. Martinez-Gonzalez, P. Vielva and M. P. Hobson
Monthly Notices of the Royal Astronomical Society 368 (1) 226 (2006)
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A full-sky prediction of the Sunyaev-Zeldovich effect from diffuse hot gas in the local universe and the upper limit from theWMAPdata

F. K. Hansen, E. Branchini, P. Mazzotta, P. Cabella and K. Dolag
Monthly Notices of the Royal Astronomical Society 361 (3) 753 (2005)
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Primordial non-Gaussianity: local curvature method and statistical significance of constraints onfNLfromWMAPdata

P. Cabella, M. Liguori, F. K. Hansen, et al.
Monthly Notices of the Royal Astronomical Society 358 (2) 684 (2005)
https://doi.org/10.1111/j.1365-2966.2005.08833.x

Separating the kinetic Sunyaev-Zel'dovich effect from primary cosmic microwave background fluctuations

O. Forni and N. Aghanim
Astronomy & Astrophysics 420 (1) 49 (2004)
https://doi.org/10.1051/0004-6361:20034110

Detection and discrimination of cosmological non-Gaussian signatures by multi-scale methods

J.-L. Starck, N. Aghanim and O. Forni
Astronomy & Astrophysics 416 (1) 9 (2004)
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The discriminating power of wavelets to detect non-Gaussianity in the cosmic microwave background

R. B. Barreiro and M. P. Hobson
Monthly Notices of the Royal Astronomical Society 327 (3) 813 (2001)
https://doi.org/10.1046/j.1365-8711.2001.04806.x