2007/12/02 by William A. Fendt, Benjamin D. Wandelt, B. D. Wandelt +2 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #Astrophysics (astro-ph) #Blind Source Separation Techniques #CCD and CMOS Imaging Sensors #FOS: Physical sciences #Radio Astronomy Observations and Technology #astro-ph
paper · pdf · doi:10.48550/arxiv.0712.0194
7 pages, 7 figures, submitted to ApJ, LaTeX with emulateapj
arxiv created 2007/12/02 · openalex publication_date 2007/12/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents the second release of Pico (Parameters for the Impatient COsmologist). Pico is a general purpose machine learning code which we have applied to computing the CMB power spectra and the WMAP likelihood. For this release, we have made improvements to the algorithm as well as the data sets used to train Pico, leading to a significant improvement in accuracy. For the 9 parameter nonflat case presented here Pico can on average compute the TT, TE and EE spectra to better than 1% of cosmic standard deviation for nearly all ℓ values over a large region of parameter space. Performing a cosmological parameter analysis of current CMB and large scale structure data, we show that these power spectra give very accurate 1 and 2 dimensional parameter posteriors. We have extended Pico to allow computation of the tensor power spectrum and the matter transfer function. Pico runs about 1500 times faster than CAMB at the default accuracy and about 250,000 times faster at high accuracy. Training Pico can be done using massively parallel computing resources, including distributed computing projects such as Cosmology@Home. On the homepage for Pico, located at http://cosmos.astro.uiuc.edu/pico, we provide new sets of regression coefficients and make the training code available for public use.