Unbiased methods for removing systematics from galaxy clustering measurements
Unbiased methods for removing systematics from galaxy clustering measurements
复制标题
从星系聚类测量中去除系统性的无偏方法
DOI：
10.1093/mnras/stv2777
复制
发表时间：
2015
影响因子：
4.8
通讯作者：
H. Peiris
中科院分区：
文献类型：
--
作者：
F. Elsner;B. Leistedt;H. Peiris
测量星系的角度群集作为红移的函数是从三维星系分布中提取信息的一种有效方法。这种测量的精度将随着正在进行和未来的宽视场星系调查而显著提高。然而，它们对观测和天体物理污染物也越来越敏感。在这里，我们研究了三种用于控制这种系统分类的方法——模板减法、基本模式投影和扩展模式投影——的统计特性，所有这些方法都利用外部提供的模板地图，旨在表征和捕捉潜在系统效应的空间变化。基于详细的数学分析，并与模拟结果一致，我们发现原始公式中的模板减法对星系角聚类的估计是有偏差的。我们推导出封闭形式的表达式，用来修正这个缺点的结果。至于基本模式投影算法，我们证明了它是没有任何偏差的，而我们得出结论，用扩展模式投影计算的结果是有偏差的。在简化的设置中，我们推导了偏差的解析表达式，并讨论了在更实际的配置中纠正偏差的选项。这三种方法的共同之处在于，尽管在不同的水平上，清理过程引起的估计量方差增加。这些结果使得在空间变化的系统中进行无偏高精度的聚类测量成为可能，这是实现当前和计划中的星系调查全部潜力的重要一步。
Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics – template subtraction, basic mode projection, and extended mode projection – all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.
影响因子：
4.8
作者：
M. Crocce;J. Carretero;A. Bauer;A. Ross;I. Sevilla-Noarbe;T. Giannantonio;F. Sobreira;J. Sánchez-J.-S
通讯作者：
M. Crocce;J. Carretero;A. Bauer;A. Ross;I. Sevilla-Noarbe;T. Giannantonio;F. Sobreira;J. Sánchez-J.-S
影响因子：
4.8
作者：
T. Giannantonio;P. Fosalba;R. Cawthon;Y. Omori;M. Crocce;F. Elsner;B. Leistedt;S. Dodelson;A. Benoit-Lévy;E. Gaztañaga;G. Holder;H. Peiris;W. Percival;D. Kirk;A. Bauer;B. Benson;G. Bernstein;J. Carretero;T. Crawford;R. Crittenden;D. Huterer;B. Jain;E. Krause;C. Reichardt;A. Ross;G. Simard;B. Soergel;A. Stark;K. Story;J. Vieira;J. Weller;T. Abbott;F. Abdalla;S. Allam;R. Armstrong;M. Banerji;R. Bernstein;E. Bertin;D. Brooks;E. Buckley-Geer;D. Burke;D. Capozzi;J. Carlstrom;A. Rosell;M. Kind;F. Castander;C. Chang;C. Cunha;L. Costa;C. D'Andrea;D. Depoy;S. Desai;H. Diehl;J. Dietrich;P. Doel;T. Eifler;A. Evrard;A. F. Neto;E. Fernandez;D. Finley;B. Flaugher;J. Frieman;D. Gerdes;D. Gruen;R. Gruendl;G. Gutiérrez;W. Holzapfel;K. Honscheid;D. James;K. Kuehn;N. Kuropatkin;O. Lahav;T. Li;M. Lima;M. March;J. Marshall;P. Martini;Peter Melchior;R. Miquel;J. Mohr;R. Nichol;B. Nord;R. Ogando;A. Plazas;A. Romer;A. Roodman;E. Rykoff;M. Sako;B. Saliwanchik;E. Sánchez;M. Schubnell;I. Sevilla-Noarbe;R. C. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;J. Thaler;D. Thomas;V. Vikram;A. Walker;R. Wechsler;J. Zuntz
通讯作者：
T. Giannantonio;P. Fosalba;R. Cawthon;Y. Omori;M. Crocce;F. Elsner;B. Leistedt;S. Dodelson;A. Benoit-Lévy;E. Gaztañaga;G. Holder;H. Peiris;W. Percival;D. Kirk;A. Bauer;B. Benson;G. Bernstein;J. Carretero;T. Crawford;R. Crittenden;D. Huterer;B. Jain;E. Krause;C. Reichardt;A. Ross;G. Simard;B. Soergel;A. Stark;K. Story;J. Vieira;J. Weller;T. Abbott;F. Abdalla;S. Allam;R. Armstrong;M. Banerji;R. Bernstein;E. Bertin;D. Brooks;E. Buckley-Geer;D. Burke;D. Capozzi;J. Carlstrom;A. Rosell;M. Kind;F. Castander;C. Chang;C. Cunha;L. Costa;C. D'Andrea;D. Depoy;S. Desai;H. Diehl;J. Dietrich;P. Doel;T. Eifler;A. Evrard;A. F. Neto;E. Fernandez;D. Finley;B. Flaugher;J. Frieman;D. Gerdes;D. Gruen;R. Gruendl;G. Gutiérrez;W. Holzapfel;K. Honscheid;D. James;K. Kuehn;N. Kuropatkin;O. Lahav;T. Li;M. Lima;M. March;J. Marshall;P. Martini;Peter Melchior;R. Miquel;J. Mohr;R. Nichol;B. Nord;R. Ogando;A. Plazas;A. Romer;A. Roodman;E. Rykoff;M. Sako;B. Saliwanchik;E. Sánchez;M. Schubnell;I. Sevilla-Noarbe;R. C. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;J. Thaler;D. Thomas;V. Vikram;A. Walker;R. Wechsler;J. Zuntz