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Title: Sample variance in weak lensing: How many simulations are required?

Abstract

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 ismore » 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. Lastly, a small number of simulations (Ns = 1 or 2) is sufficient to forecast parameter confidence intervals at percent accuracy.« less

Authors:
 [1];  [2];  [3]
  1. Columbia Univ., New York, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Columbia Univ., New York, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1245392
Alternate Identifier(s):
OSTI ID: 1243154
Report Number(s):
BNL-111951-2016-JA
Journal ID: ISSN 2470-0010; PRVDAQ; KA2301020
Grant/Contract Number:  
SC00112704; AC02-98CH10886; SC0012704; ACI-1053575
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review D
Additional Journal Information:
Journal Volume: 93; Journal Issue: 6; Journal ID: ISSN 2470-0010
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Petri, Andrea, May, Morgan, and Haiman, Zoltan. Sample variance in weak lensing: How many simulations are required?. United States: N. p., 2016. Web. doi:10.1103/PhysRevD.93.063524.
Petri, Andrea, May, Morgan, & Haiman, Zoltan. Sample variance in weak lensing: How many simulations are required?. United States. https://doi.org/10.1103/PhysRevD.93.063524
Petri, Andrea, May, Morgan, and Haiman, Zoltan. Thu . "Sample variance in weak lensing: How many simulations are required?". United States. https://doi.org/10.1103/PhysRevD.93.063524. https://www.osti.gov/servlets/purl/1245392.
@article{osti_1245392,
title = {Sample variance in weak lensing: How many simulations are required?},
author = {Petri, Andrea and May, Morgan and Haiman, Zoltan},
abstractNote = {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. Lastly, a small number of simulations (Ns = 1 or 2) is sufficient to forecast parameter confidence intervals at percent accuracy.},
doi = {10.1103/PhysRevD.93.063524},
journal = {Physical Review D},
number = 6,
volume = 93,
place = {United States},
year = {Thu Mar 24 00:00:00 EDT 2016},
month = {Thu Mar 24 00:00:00 EDT 2016}
}

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