期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 454, 期 3, 页码 2451-2471出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stv2000
关键词
cosmological parameters; cosmology: theory; large-scale structure of Universe
资金
- National Science Foundation [1066293, AST 0806367, AST 1108802]
- hospitality of the Aspen Center for Physics
- US Department of Energy [DE-FG02-95ER40896]
- Pittsburgh Particle physics, Astrophysics, and Cosmology Center (PITT PACC) at the University of Pittsburgh
- Scientific Discovery through Advanced Computing (SciDAC) programme - US Department of Energy, Office of Science, Advanced Scientific Computing Research and High Energy Physics
- National Aeronautics and Space Administration
- Direct For Mathematical & Physical Scien
- Division Of Physics [1125897] Funding Source: National Science Foundation
Systematic uncertainties that have been subdominant in past large-scale structure (LSS) surveys are likely to exceed statistical uncertainties of current and future LSS data sets, potentially limiting the extraction of cosmological information. Here we present a general framework (Principal Component Analysis - PCA - marginalization) to consistently incorporate systematic effects into a likelihood analysis. This technique naturally accounts for degeneracies between nuisance parameters and can substantially reduce the dimension of the parameter space that needs to be sampled. As a practical application, we apply PCA marginalization to account for baryonic physics as an uncertainty in cosmic shear tomography. Specifically, we use COSMOLIKE to run simulated likelihood analyses on three independent sets of numerical simulations, each covering a wide range of baryonic scenarios differing in cooling, star formation, and feedback mechanisms. We simulate a Stage III (Dark Energy Survey) and Stage IV (Large Synoptic Survey Telescope/Euclid) survey and find a substantial bias in cosmological constraints if baryonic physics is not accounted for. We then show that PCA marginalization (employing at most three to four nuisance parameters) removes this bias. Our study demonstrates that it is possible to obtain robust, precise constraints on the dark energy equation of state even in the presence of large levels of systematic uncertainty in astrophysical processes. We conclude that the PCA marginalization technique is a powerful, general tool for addressing many of the challenges facing the precision cosmology programme.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据