{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/07120194","title":"Computing High Accuracy Power Spectra with Pico","arxiv_id":"0712.0194","date":"2007-12-02","proceeding":null,"authors":["William A. Fendt","Benjamin D. Wandelt"],"abstract":"This paper presents the second release of Pico (Parameters for the Impatient\nCOsmologist). Pico is a general purpose machine learning code which we have\napplied to computing the CMB power spectra and the WMAP likelihood. For this\nrelease, we have made improvements to the algorithm as well as the data sets\nused to train Pico, leading to a significant improvement in accuracy. For the 9\nparameter nonflat case presented here Pico can on average compute the TT, TE\nand EE spectra to better than 1% of cosmic standard deviation for nearly all\n$\\ell$ values over a large region of parameter space. Performing a cosmological\nparameter analysis of current CMB and large scale structure data, we show that\nthese power spectra give very accurate 1 and 2 dimensional parameter\nposteriors. We have extended Pico to allow computation of the tensor power\nspectrum and the matter transfer function. Pico runs about 1500 times faster\nthan CAMB at the default accuracy and about 250,000 times faster at high\naccuracy. Training Pico can be done using massively parallel computing\nresources, including distributed computing projects such as Cosmology@Home. On\nthe homepage for Pico, located at http://cosmos.astro.uiuc.edu/pico, we provide\nnew sets of regression coefficients and make the training code available for\npublic use.","url_abs":"http://arxiv.org/abs/0712.0194v1","url_pdf":"http://arxiv.org/pdf/0712.0194v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"07120194","repo_url":"https://github.com/marius311/pypico","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":"pico","task_name":"PICO"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}