Going beyond the galaxy power spectrum: An analysis of BOSS data with wavelet scattering transforms
Going beyond the galaxy power spectrum: An analysis of BOSS data with wavelet scattering transforms
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DOI：
10.1103/physrevd.106.103509
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发表时间：
2022-04
影响因子：
5
通讯作者：
Georgios Valogiannis;C. Dvorkin
中科院分区：
文献类型：
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作者：
Georgios Valogiannis;C. Dvorkin
我们执行的小波散射变换（WST）的实际星系观测的第一个应用程序，通过WST分析的BOSS DR 12 CMASS数据集。我们包括红移空间各向异性的影响，非平凡的调查几何，系统的权重，和阿尔科克-Paczynski失真的影响，功率谱分析的常用步骤。为了捕捉WST的宇宙学依赖性，我们使用从最先进的ABACUSSUMMIT模拟中获得的星系模拟，调整以匹配BOSS CMASS样本在红移范围$0.46<z<0.60$的各向异性相关函数。使用我们的模型的WST系数，以及前2个多极的星系功率谱，我们作为参考，我们执行的CMASS数据的可能性分析。我们得到了4个宇宙学参数的后验概率分布，$\{\omega_b，\omega_c，ns，\sigma_8\}$，以及哈勃常数，来自于一个固定的值的声音视界的角尺寸在最后散射的普朗克卫星，所有这些都是边缘化的7个讨厌的参数的晕占据分布模型。WST被发现提供一个实质性的改善，预测的$1\sigma$错误的值相比，定期的功率谱，这是更紧密的一个因素为$3-5$的情况下，平坦的和无信息的先验和由一个因素为$3-8$，当大爆炸核合成前应用于$\omega_B$的值。我们的结果是调查性的，并且受到某些近似的影响，我们将在文中讨论这些结果。
We perform the first application of the wavelet scattering transform (WST) to actual galaxy observations, through a WST analysis of the BOSS DR12 CMASS dataset. We included the effects of redshift-space anisotropy, non-trivial survey geometry, systematic weights, and the Alcock-Paczynski distortion effect, following the commonly adopted steps for the power spectrum analysis. In order to capture the cosmological dependence of the WST, we use galaxy mocks obtained from the state-of-the-art ABACUSSUMMIT simulations, tuned to match the anisotropic correlation function of the BOSS CMASS sample in the redshift range $0.46<z<0.60$. Using our model for the WST coefficients, as well as for the first 2 multipoles of the galaxy power spectrum, that we use as reference, we perform a likelihood analysis of the CMASS data. We obtain the posterior probability distributions of 4 cosmological parameters, $\{\omega_b,\omega_c,n_s,\sigma_8\}$, as well as the Hubble constant, derived from a fixed value of the angular size of the sound horizon at last scattering measured by the Planck satellite, all of which are marginalized over the 7 nuisance parameters of the Halo Occupation Distribution model. The WST is found to deliver a substantial improvement in the values of the predicted $1\sigma$ errors compared to the regular power spectrum, which are tighter by a factor of $3-5$ in the case of flat and uninformative priors and by a factor of $3-8$, when a Big Bang Nucleosynthesis prior is applied on the value of $\omega_b$. Our results are investigative and subject to certain approximations, which we discuss in the text.