BAO from angular clustering: optimization and mitigation of theoretical systematics
BAO from angular clustering: optimization and mitigation of theoretical systematics
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DOI：
10.1093/mnras/sty2036
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发表时间：
2018-01
影响因子：
4.8
通讯作者：
K. Chan;M. Crocce;A. Ross;S. Ávila;J. Elvin-Poole;M. Manera;W. Percival;R. Rosenfeld;T. Abbott;F. Abdalla;S. Allam;E. Bertin;D. Brooks;D. Burke;A. Rosell;M. Kind;J. Carretero;F. Castander;C. Cunha;C. D'Andrea;L. Costa;C. Davis;J. Vicente;T. Eifler;J. Estrada;B. Flaugher;P. Fosalba;J. Frieman;J. García-Bellido;E. Gaztañaga;D. Gerdes;D. Gruen;R. Gruendl;J. Gschwend;G. Gutiérrez;W. Hartley;K. Honscheid;B. Hoyle;D. James;E. Krause;K. Kuehn;O. Lahav;M. Lima;M. March;F. Menanteau;C. Miller;R. Miquel;A. Plazas;K. Reil;A. Roodman;E. Sánchez;V. Scarpine;I. Sevilla-Noarbe;M. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;D. Thomas;A. Walker
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文献类型：
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作者：
K. Chan;M. Crocce;A. Ross;S. Ávila;J. Elvin-Poole;M. Manera;W. Percival;R. Rosenfeld;T. Abbott;F. Abdalla;S. Allam;E. Bertin;D. Brooks;D. Burke;A. Rosell;M. Kind;J. Carretero;F. Castander;C. Cunha;C. D'Andrea;L. Costa;C. Davis;J. Vicente;T. Eifler;J. Estrada;B. Flaugher;P. Fosalba;J. Frieman;J. García-Bellido;E. Gaztañaga;D. Gerdes;D. Gruen;R. Gruendl;J. Gschwend;G. Gutiérrez;W. Hartley;K. Honscheid;B. Hoyle;D. James;E. Krause;K. Kuehn;O. Lahav;M. Lima;M. March;F. Menanteau;C. Miller;R. Miquel;A. Plazas;K. Reil;A. Roodman;E. Sánchez;V. Scarpine;I. Sevilla-Noarbe;M. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;D. Thomas;A. Walker
本文研究了利用层析仓中的角关联函数测量重子声振荡（BAO）的方法学和潜在的理论体系。我们使用1800个模拟来校准和优化暗能量调查第1年数据集的管道。我们比较了BAO拟合结果与三个估计：最大似然估计（MLE），配置文件似然估计，和马尔可夫链蒙特卡罗。MLE的拟合结果偏差最小，并且在去除具有未检测到的BAO信号的极端模拟之后，其导出的la误差棒最接近高斯分布值。我们表明，在构建模板，如从宇宙学的模拟或潜在的photo-z错误的不匹配的不正确的假设，可能会导致BAO角位移。我们发现，MLE是最好的跟踪这种系统偏差的方法，允许恢复真实的角距离值。在真实的调查分析中，可能会发生最终数据样本属性与模拟目录的属性略有不同的情况。我们表明，由于样本差异对模拟协方差的影响可以通过高斯协方差矩阵的帮助或更有效地使用模拟协方差的本征模展开来校正。在本征模展开中，本征模由一些代理协方差矩阵提供。本征模展开是显着不太容易受到统计波动相对于直接测量的协方差矩阵，因为自由参数的数量大大减少。
We study the methodology and potential theoretical systematics of measuring baryon acoustic oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 data set using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov Chain Monte Carlo. The fit results from the MLE arc the least biased and their derived la error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAO signal. We show that incorrect assumptions in constructing the template, such as mismatches from the cosmology of the mocks or the underlying photo-z errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalogue. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced.