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arXiv 2103.09247astro-ph.COastro-ph.IM
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弱引力透镜散射变换:暗能量与中微子质量灵敏度

Weak lensing scattering transform: dark energy and neutrino mass sensitivity

Sihao Cheng, Brice Ménard

AI总结:

本文扩展散射变换在弱引力透镜中的应用,证明其在刻画透镜质量图非高斯特征及暗能量、中微子质量参数推断上优于功率谱和双谱,且散射系数具有更高斯化的似然分布,是更优选的统计量。

摘要:

随着弱引力透镜巡天观测不断深入,会聚场展现出更多非高斯特征,这些特征只能通过超越功率谱的统计量来提取。在 Cheng 等人(2020)的工作中,我们证明了散射变换——一种借鉴卷积神经网络数学概念的新型统计量——是在非高斯区域进行宇宙学参数估计的有力工具。本文中,我们扩展了该分析,利用弱引力透镜巡天探索其对暗能量和中微子质量参数的灵敏度。我们首先使用图像合成直观地展示,与功率谱和双谱相比,散射变换提供了更好的统计词汇来刻画透镜质量图的感知特性。随后我们证明它也更适合参数推断:(i)在无噪声条件下提供更高的灵敏度;(ii)在 Rubin 类巡天的噪声水平下,尽管约束并未显著紧于双谱,但散射系数具有更高斯化的采样分布,这对于似然参数化和精确的宇宙学推断而言是一个重要性质。我们认为,综合考虑约束能力和似然性质,散射系数是更优选的统计量。

英文摘要:

As weak lensing surveys become deeper, they reveal more non-Gaussian aspects of the convergence field which can only be extracted using statistics beyond the power spectrum. In Cheng et al. (2020) we showed that the scattering transform, a novel statistic borrowing mathematical concepts from convolutional neural networks, is a powerful tool for cosmological parameter estimation in the non-Gaussian regime. Here, we extend that analysis to explore its sensitivity to dark energy and neutrino mass parameters with weak lensing surveys. We first use image synthesis to show visually that, compared to the power spectrum and bispectrum, the scattering transform provides a better statistical vocabulary to characterize the perceptual properties of lensing mass maps. We then show that it is also better suited for parameter inference: (i) it provides higher sensitivity in the noiseless regime, and (ii) at the noise level of Rubin-like surveys, though the constraints are not significantly tighter than those of the bispectrum, the scattering coefficients have a more Gaussian sampling distribution, which is an important property for likelihood parametrization and accurate cosmological inference. We argue that the scattering coefficients are preferred statistics considering both constraining power and likelihood properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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