大偏差理论孔径质量场的概率分布函数

A. Barthelemy, S. Codis, F. Bernardeau
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引用次数: 8

摘要

在层析宇宙剪切测量的背景下,建立了孔径质量(Map)的一点统计理论模型。这种形式是基于将大偏差原理应用于投射的物质密度场,更具体地说,应用于角孔径质量。后者的优点是可以直接从观测到的剪切场中提取可观测值,并且通过构造独立于长波模态。此外,我们表明,在基于所谓的BNT变换的零化过程的帮助下,可以构建仅依赖于有限范围的红移的可观测值,使它们独立于小尺度模态。这一过程可以非常准确地预测这种可观测点的单点概率分布函数的形状,与之前获得的3D可观测点的形状相当。然而,与特定模拟的比较揭示了不一致的结果,表明合成透镜图对这种精细的观测结果不够精确。它指出需要更精确的专用数值开发,其性能可以用这些可观察到的数据作为基准。我们进一步回顾了可能在未来弱透镜调查(如欧几里得)中影响这种形式的系统分类,特别是形状噪声的影响以及来自透镜-透镜耦合、测地线偏差、减少剪切和放大偏差的领先修正。
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Probability distribution function of the aperture mass field with large deviation theory
In the context of tomographic cosmic shear surveys, a theoretical model for the one-point statistics of the aperture mass (Map) is developed. This formalism is based on the application of the large deviation principle to the projected matter density field and more specifically to the angular aperture masses. The latter holds the advantage of being an observable that can be directly extracted from the observed shear field and to be, by construction, independent from the long wave modes. Furthermore we show that, with the help of a nulling procedure based on the so-called BNT transform, it is possible to build observables that depend only on a finite range of redshifts making them also independent from the small-scale modes. This procedure makes predictions for the shape of the one-point Probability Distribution Function of such an observable very accurate, comparable to what had been previously obtained for 3D observables. Comparisons with specific simulations reveal however inconsistent results showing that synthetic lensing maps were not accurate enough for such refined observables. It points to the need for more precise dedicated numerical developments whose performances could be benchmarked with such observables. We furthermore review the possible systematics that could affect such a formalism in future weak-lensing surveys like Euclid, notably the impact of shape noise as well as leading corrections coming from lens-lens couplings, geodesic deviation, reduced shear and magnification bias.
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