{"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/efficient-power-spectrum-estimation-for-high","title":"Efficient Power Spectrum Estimation for High Resolution CMB Maps","arxiv_id":"0809.1092","date":"2008-09-05","proceeding":null,"authors":["Sudeep Das","Amir Hajian","David N. Spergel"],"abstract":"Estimation of the angular power spectrum of the Cosmic Microwave Background\n(CMB) on a small patch of sky is usually plagued by serious spectral leakage,\nspecially when the map has a hard edge. Even on a full sky map, point source\nmasks can alias power from large scales to small scales producing excess\nvariance at high multipoles. We describe a new fast, simple and local method\nfor estimation of power spectra on small patches of the sky that minimizes\nspectral leakage and reduces the variance of the spectral estimate. For\nexample, when compared with the standard uniform sampling approach on a 8\ndegree X 8 degree patch of the sky with 2% area masked due to point sources,\nour estimator halves the errorbars at l=2000 and achieves a more than fourfold\nreduction in errorbars at l=3500. Thus, a properly analyzed experiment will\nhave errorbars at l=3500 equivalent to those of an experiment analyzed with the\nnow standard technique with ~ 16-25 times the integration time.","url_abs":"http://arxiv.org/abs/0809.1092v1","url_pdf":"http://arxiv.org/pdf/0809.1092v1.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":"efficient-power-spectrum-estimation-for-high","repo_url":"https://github.com/sudeepdas/flipper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"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}