Mapping variations of redshift distributions with probability integral transforms
Mapping variations of redshift distributions with probability integral transforms
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DOI：
10.1093/mnras/stac3585
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发表时间：
2022-10
影响因子：
4.8
通讯作者：
J. Myles;D. Gruen;A. Amon;A. Alarcon;J. DeRose;S. Everett;S. Dodelson;G. Bernstein;A. Campos;I. Harrison;N. MacCrann;J. McCullough;M. Raveri;C. S'anchez;M. Troxel;Biao Yin;T. Abbott;S. Allam;O. Alves;F. Andrade-Oliveira;E. Bertin;D. Brooks;D. Burke;A. Rosell;M. Kind;J. Carretero;R. Cawthon;M. Costanzi;L. Costa;M. Pereira;S. Desai;P. Doel;I. Ferrero;B. Flaugher;J. Frieman;J. Garc'ia-Bellido;M. Gatti;D. Gerdes;R. Gruendl;J. Gschwend;G. Gutiérrez;W. Hartley;S. Hinton;D. Hollowood;K. Honscheid;D. James;K. Kuehn;O. Lahav;Peter Melchior;J. Mena-Fern'andez;F. Menanteau;R. Miquel;J. Mohr;A. Palmese;F. Paz-Chinch'on;A. Pieres;A. A. P. Malag'on-A.;J. Prat;M. Rodríguez-Monroy;E. Sanchez;V. Scarpine;I. Sevilla-Noarbe;M. Smith;E. Suchyta;M. Swanson;G. Tarlé;D. Tucker;M. Vincenzi;N. Weaverdyck
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作者：
J. Myles;D. Gruen;A. Amon;A. Alarcon;J. DeRose;S. Everett;S. Dodelson;G. Bernstein;A. Campos;I. Harrison;N. MacCrann;J. McCullough;M. Raveri;C. S'anchez;M. Troxel;Biao Yin;T. Abbott;S. Allam;O. Alves;F. Andrade-Oliveira;E. Bertin;D. Brooks;D. Burke;A. Rosell;M. Kind;J. Carretero;R. Cawthon;M. Costanzi;L. Costa;M. Pereira;S. Desai;P. Doel;I. Ferrero;B. Flaugher;J. Frieman;J. Garc'ia-Bellido;M. Gatti;D. Gerdes;R. Gruendl;J. Gschwend;G. Gutiérrez;W. Hartley;S. Hinton;D. Hollowood;K. Honscheid;D. James;K. Kuehn;O. Lahav;Peter Melchior;J. Mena-Fern'andez;F. Menanteau;R. Miquel;J. Mohr;A. Palmese;F. Paz-Chinch'on;A. Pieres;A. A. P. Malag'on-A.;J. Prat;M. Rodríguez-Monroy;E. Sanchez;V. Scarpine;I. Sevilla-Noarbe;M. Smith;E. Suchyta;M. Swanson;G. Tarlé;D. Tucker;M. Vincenzi;N. Weaverdyck
我们提出了一种方法，用于映射概率分布函数之间的变化，并应用这种方法的背景下测量星系的红移分布从成像调查数据。这种方法，我们将其命名为PITPZ，因为它依赖于概率积分变换，它使用集合中分布函数之间的曲线差异作为应用于另一个分布函数的变换，从而将集合中的变化转移到后者的分布函数。这一过程广泛适用于不确定性传播问题。例如，在红移分布的背景下，由于某些效应的不确定性贡献只能在模拟中有效地研究，因此需要将模拟中测量的变化转移到从数据测量的红移分布。我们说明了使用PITPZ的方法来传播光度校准不确定性的红移分布的暗能量巡天3年弱透镜源星系。对于这个测试案例，我们发现，PITPZ产生的透镜振幅不确定性估计，由于光度校准误差在1%的真理，相比之下，高达30%的低估，当使用传统方法。
We present a method for mapping variations between probability distribution functions and apply this method within the context of measuring galaxy redshift distributions from imaging survey data. This method, which we name PITPZ for the probability integral transformations it relies on, uses a difference in curves between distribution functions in an ensemble as a transformation to apply to another distribution function, thus transferring the variation in the ensemble to the latter distribution function. This procedure is broadly applicable to the problem of uncertainty propagation. In the context of redshift distributions, for example, the uncertainty contribution due to certain effects can be studied effectively only in simulations, thus necessitating a transfer of variation measured in simulations to the redshift distributions measured from data. We illustrate the use of PITPZ by using the method to propagate photometric calibration uncertainty to redshift distributions of the Dark Energy Survey Year 3 weak lensing source galaxies. For this test case, we find that PITPZ yields a lensing amplitude uncertainty estimate due to photometric calibration error within 1 per cent of the truth, compared to as much as a 30 per cent underestimate when using traditional methods.