2019-10-14T04:17:28Z
https://digital.csic.es/dspace-oai/request
oai:digital.csic.es:10261/45906
2018-06-25T07:32:19Z
com_10261_37
com_10261_4
col_10261_290
Casaponsa, Biuse
Bridges, M.
Curto, Andrés
Barreiro, R. Belén
Hobson, M. P.
Martínez-González, Enrique
2012-02-20T11:04:10Z
2012-02-20T11:04:10Z
2011-09
Monthly Notices of the Royal Astronomical Society 41681): 457-464 (2011)
0035-8711
http://hdl.handle.net/10261/45906
10.1111/j.1365-2966.2011.19053.x
1365-2966
We present a multiclass neural network (NN) classifier as a method to measure non-Gaussianity, characterized by the local non-linear coupling parameter fNL, in maps of the cosmic microwave background (CMB) radiation. The classifier is trained on simulated non-Gaussian CMB maps with a range of known fNL values by providing it with wavelet coefficients of the maps; we consider both the HEALPix wavelet (HW) and the spherical Mexican hat wavelet (SMHW). When applied to simulated test maps, the NN classifier produces results in very good agreement with those obtained using standard χ2 minimization. The standard deviations of the fNL estimates for Wilkinson Microwave Anisotropy Probe1 like simulations were σ= 22 and 33 for the SMHW and the HW, respectively, which are extremely close to those obtained using classical statistical methods in Curto et al. and Casaponsa et al. Moreover, the NN classifier does not require the inversion of a large covariance matrix, thus avoiding any need to regularize the matrix when it is not directly invertible, and is considerably faster.
eng
openAccess
Methods: data analysis
Cosmic background radiation
Constraints on fNL from Wilkinson Microwave Anisotropy Probe 7-year data using a neural network classifier
artículo