论文标题
BACCO仿真项目：红移空间中的BACCO混合拉格朗日偏置扩展模型
The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space
论文作者
论文摘要
我们提出了一个模拟器，该模拟器可以准确预测红移空间中星系的功率谱，这是宇宙学参数的函数。我们的仿真器基于二阶拉格朗日偏置扩展，该扩展使用宇宙学$ n $ body模拟转移到欧拉空间。然后，使用模拟颗粒和光环的非线性速度场印刷红移空间变形。我们使用经过BACCO项目的模拟训练的前向神经网络构建模拟器，该网络涵盖了8维参数空间，包括大量中微子和动态性暗能量。我们表明，我们的仿真器从单极，四极杆和十六进制的模拟星系目录中提供了无偏的宇宙学约束，这些目录模仿了boss-class样本到非线性尺度（$ k \ sim0.6 $ [$ h/$ h/$ mpc] $^{3} $）。这项工作打开了使用对宇宙大规模结构的观察来从小尺度上鲁棒学信息的可能性。
We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an 8-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe.
