The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
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DOI：
10.21105/astro.2207.05202
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发表时间：
2022-07
影响因子：
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通讯作者：
T. Makinen;T. Charnock;P. Lemos;Natalia Porqueres;A. Heavens;Benjamin Dan Wandelt
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文献类型：
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作者：
T. Makinen;T. Charnock;P. Lemos;Natalia Porqueres;A. Heavens;Benjamin Dan Wandelt
我们提出了一个隐式似然方法来量化宇宙信息的离散目录数据，组装成图。要做到这一点，我们探索宇宙学参数的约束，使用模拟暗物质晕目录。我们采用信息最大化神经网络（IMNNs）量化Fisher信息提取作为图形表示的函数。我们a）证明了模块图结构在无噪声限制下对底层宇宙学的高灵敏度，B）通过与传统统计学的比较，证明了图神经网络摘要自动联合收割机结合质量和聚类信息，c）证明了当目录受到噪声调查切割时，网络仍然可以提取信息
We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter constraints using mock dark matter halo catalogues. We employ Information Maximising Neural Networks (IMNNs) to quantify Fisher information extraction as a function of graph representation. We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts