Foreground removal from WMAP 5 yr temperature maps using an MLP neural network

Research output: Contribution to journalJournal articleResearchpeer-review

329 Downloads (Pure)

Abstract

Aims. One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5 yr temperature data without using any auxiliary data. Results. A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions. With these results the neural network method is well prepared for dealing with the high-quality CMB data from the ESA Planck Surveyor satellite. © ESO, 2010.
Original languageEnglish
JournalAstronomy & Astrophysics
Volume520
Pages (from-to)A87
Number of pages6
ISSN0004-6361
DOIs
Publication statusPublished - 2010

Keywords

  • Radio continuum: general
  • Radio continuum: ISM
  • Cosmic background radiation
  • Methods: data analysis

Fingerprint Dive into the research topics of 'Foreground removal from WMAP 5 yr temperature maps using an MLP neural network'. Together they form a unique fingerprint.

Cite this