19 research outputs found

    Foreground removal from CMB temperature maps using an MLP neural network

    Full text link
    One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.Comment: Accepted for publication in Astrophysics and Space Scienc

    Aerosol speciation and mass prediction from toluene oxidation under high NOx conditions

    No full text
    A kinetically based gas-particle partitioning box model is used to highlight the importance of parameter representation in the prediction of secondary organic aerosol (SOA) formation following the photo-oxidation of toluene. The model is initialized using experimental data from York University's indoor smog chamber and provides a prediction of the total aerosol yield and speciation. A series of model sensitivity experiments were performed to study the aerosol speciation and mass prediction under high NOx conditions (VOC/NOx = 0.2). Sensitivity experiments indicate vapour pressure estimation to be a large area of weakness in predicting aerosol mass, creating an average total error range of 70 μg m−3 (range of 5–145 μg m−3), using two different estimation methods. Aerosol speciation proved relatively insensitive to changes in vapour pressure. One species, 3-methyl-6-nitro-catechol, dominated the aerosol phase regardless of the vapour pressure parameterization used and comprised 73–88% of the aerosol by mass. The dominance is associated with the large concentration of 3-methyl-6-nitro-catechol in the gas-phase. The high NOx initial conditions of this study suggests that the predominance of 3-methyl-6-nitro-catechol likely results from the cresol-forming branch in the Master Chemical Mechanism taking a significant role in secondary organic aerosol formation under high NOx conditions. Further research into the yields and speciation leading to this reaction product is recommended
    corecore