论文标题

COSMOGRIDV1:一个模拟的$ W $ CDM理论预测地图级宇宙学推断

CosmoGridV1: a simulated $w$CDM theory prediction for map-level cosmological inference

论文作者

Kacprzak, Tomasz, Fluri, Janis, Schneider, Aurel, Refregier, Alexandre, Stadel, Joachim

论文摘要

我们提出了CosmogridV1:一套大量的LightCone模拟,用于具有大规模结构探针的地图级宇宙学推断。它设计用于基于III期光度测量的宇宙参数测量,并具有非高斯统计和机器学习。 COSMOGRIDV1跨越$ W $ CDM型号,通过更改$ω_m$,$σ_8$,$ W_0 $,$ H_0 $,$ N_S $,$ n_s $,$ω_b$,并假设具有三个具有$ sumM_ν$ sumM_ν$ = 0.06 $的中微子。该空间被SOBOL序列上的2500个网格点覆盖。在每个网格点,我们使用PKDGRAV3进行7个模拟,并在Nside = 2048的69个粒子图存储至$ z $ = 3.5,以及Halo Catalog快照。基准宇宙学具有200个独立的模拟以及它们的模具衍生物。 Cosmogridv1的重要部分是28个模拟的基准集,其中包括较大的盒子,较高的粒子计数和壳的较高的红移分辨率。他们允许测试新的分析类型的分析是否对CosmogridV1中的选择敏感。我们使用基于壳的重子校正模型在地图级别上添加了Baryon反馈效果。这些壳用于创建使用UFALCON代码的弱重力透镜,内在对齐和星系聚类的地图。 Cosmogridv1的主要部分是原始粒子计数壳,可用于为给定的$ N(Z)$创建全套地图。我们还发布了III阶段预测的投影图,以及以前使用Cosmogridv1的深度学习约束中使用的地图。数据可在www.cosmogrid.ai上找到。

We present CosmoGridV1: a large set of lightcone simulations for map-level cosmological inference with probes of large scale structure. It is designed for cosmological parameter measurement based on Stage-III photometric surveys with non-Gaussian statistics and machine learning. CosmoGridV1 spans the $w$CDM model by varying $Ω_m$, $σ_8$, $w_0$, $H_0$, $n_s$, $Ω_b$, and assumes three degenerate neutrinos with $\sum m_ν$ = 0.06 eV. This space is covered by 2500 grid points on a Sobol sequence. At each grid point, we run 7 simulations with PkdGrav3 and store 69 particle maps at nside=2048 up to $z$=3.5, as well as halo catalog snapshots. The fiducial cosmology has 200 independent simulations, along with their stencil derivatives. An important part of CosmoGridV1 is the benchmark set of 28 simulations, which include larger boxes, higher particle counts, and higher redshift resolution of shells. They allow for testing if new types of analyses are sensitive to choices made in CosmoGridV1. We add baryon feedback effects on the map level, using shell-based baryon correction model. The shells are used to create maps of weak gravitational lensing, intrinsic alignment, and galaxy clustering, using the UFalcon code. The main part of CosmoGridV1 are the raw particle count shells that can be used to create full-sky maps for a given $n(z)$. We also release projected maps for a Stage-III forecast, as well as maps used previously in KiDS-1000 deep learning constraints with CosmoGridV1. The data is available at www.cosmogrid.ai.

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