Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split
arxiv(2024)
摘要
We present a new class of machine-learning emulators that accurately model
the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space
correlation functions in the context of Rubin Observatory year one simulated
data. To illustrate its capabilities in forecasting models beyond the standard
ΛCDM, we forecast how well LSST Year 1 data will be able to probe the
consistency between geometry Ω^ geo_m and growth
Ω^ growth_m dark matter densities in the so-called split
ΛCDM parameterization. When trained with a few million samples, our
emulator shows uniform accuracy across a wide range in an 18-dimensional
parameter space. We provide a detailed comparison of three neural network
designs, illustrating the importance of adopting state-of-the-art Transformer
blocks. Our study also details their performance when computing Bayesian
evidence for cosmic shear on three fiducial cosmologies. The transformers-based
emulator is always accurate within PolyChord's precision. As an application, we
use our emulator to study the degeneracies between dark energy models and
growth geometry split parameterizations. We find that the growth-geometry split
remains to be a meaningful test of the smooth dark energy assumption.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要