{"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.1601.06792","open_access":"1"}],"day":"24","status":"public","language":[{"iso":"eng"}],"issue":"6","date_published":"2016-03-24T00:00:00Z","article_type":"original","author":[{"last_name":"Petri","first_name":"Andrea","full_name":"Petri, Andrea"},{"full_name":"Haiman, Zoltán","first_name":"Zoltán","last_name":"Haiman","id":"7c006e8c-cc0d-11ee-8322-cb904ef76f36"},{"last_name":"May","full_name":"May, Morgan","first_name":"Morgan"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-09-05T13:57:57Z","article_number":"063524","citation":{"chicago":"Petri, Andrea, Zoltán Haiman, and Morgan May. “Sample Variance in Weak Lensing: How Many Simulations Are Required?” <i>Physical Review D</i>. American Physical Society, 2016. <a href=\"https://doi.org/10.1103/physrevd.93.063524\">https://doi.org/10.1103/physrevd.93.063524</a>.","short":"A. Petri, Z. Haiman, M. May, Physical Review D 93 (2016).","ama":"Petri A, Haiman Z, May M. Sample variance in weak lensing: How many simulations are required? <i>Physical Review D</i>. 2016;93(6). doi:<a href=\"https://doi.org/10.1103/physrevd.93.063524\">10.1103/physrevd.93.063524</a>","apa":"Petri, A., Haiman, Z., &#38; May, M. (2016). Sample variance in weak lensing: How many simulations are required? <i>Physical Review D</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevd.93.063524\">https://doi.org/10.1103/physrevd.93.063524</a>","ista":"Petri A, Haiman Z, May M. 2016. Sample variance in weak lensing: How many simulations are required? Physical Review D. 93(6), 063524.","ieee":"A. Petri, Z. Haiman, and M. May, “Sample variance in weak lensing: How many simulations are required?,” <i>Physical Review D</i>, vol. 93, no. 6. American Physical Society, 2016.","mla":"Petri, Andrea, et al. “Sample Variance in Weak Lensing: How Many Simulations Are Required?” <i>Physical Review D</i>, vol. 93, no. 6, 063524, American Physical Society, 2016, doi:<a href=\"https://doi.org/10.1103/physrevd.93.063524\">10.1103/physrevd.93.063524</a>."},"date_updated":"2024-09-24T09:17:03Z","oa_version":"Preprint","publisher":"American Physical Society","type":"journal_article","scopus_import":"1","month":"03","quality_controlled":"1","doi":"10.1103/physrevd.93.063524","publication_identifier":{"issn":["2470-0010","2470-0029"]},"publication":"Physical Review D","year":"2016","oa":1,"extern":"1","_id":"17628","volume":93,"title":"Sample variance in weak lensing: How many simulations are required?","intvolume":"        93","publication_status":"published","abstract":[{"lang":"eng","text":"Constraining cosmology using weak gravitational lensing consists of comparing a measured feature vector of dimension 𝑁𝑏 with its simulated counterpart. An accurate estimate of the 𝑁𝑏×𝑁𝑏 feature covariance matrix 𝐂 is essential to obtain accurate parameter confidence intervals. When 𝐂 is measured from a set of simulations, an important question is how large this set should be. To answer this question, we construct different ensembles of 𝑁𝑟 realizations of the shear field, using a common randomization procedure that recycles the outputs from a smaller number 𝑁𝑠≤𝑁𝑟 of independent ray-tracing 𝑁-body simulations. We study parameter confidence intervals as a function of (𝑁𝑠, 𝑁𝑟) in the range 1≤𝑁𝑠≤200 and 1≤𝑁𝑟≲105. Previous work [S. Dodelson and M. D. Schneider, Phys. Rev. D 88, 063537 (2013)] has shown that Gaussian noise in the feature vectors (from which the covariance is estimated) lead, at quadratic order, to an 𝑂⁢(1/𝑁𝑟) degradation of the parameter confidence intervals. Using a variety of lensing features measured in our simulations, including shear-shear power spectra and peak counts, we show that cubic and quartic covariance fluctuations lead to additional 𝑂⁢(1/𝑁2𝑟) error degradation that is not negligible when 𝑁𝑟 is only a factor of few larger than 𝑁𝑏. We study the large 𝑁𝑟 limit, and find that a single, 240  Mpc/ℎ sized 5123-particle 𝑁-body simulation (𝑁𝑠=1) can be repeatedly recycled to produce as many as 𝑁𝑟=few×104 shear maps whose power spectra and high-significance peak counts can be treated as statistically independent. As a result, a small number of simulations (𝑁𝑠=1 or 2) is sufficient to forecast parameter confidence intervals at percent accuracy."}],"external_id":{"arxiv":["1601.06792"]},"article_processing_charge":"No"}