The standard ΛCDM cosmological model predicts the presence of cold dark matter, with the current accelerated expansion of the Universe driven by dark energy. This model has recently come under scrutiny because of tensions in measurements of the expansion and growth histories of the Universe, parameterized using H₀ and S₈. The three-dimensional clustering of galaxies encodes key cosmological information that addresses these tensions. Here we present a set of cosmological constraints using simulation-based inference that exploits additional non-Gaussian information on nonlinear scales from galaxy clustering, inaccessible with current analyses. We analyse a subset of the Baryon Oscillation Spectroscopic Survey (BOSS) galaxy survey using SimBIG, a new framework for cosmological inference that leverages high-fidelity simulations and deep generative models. We use two clustering statistics beyond the standard power spectrum: the bispectrum and a summary of the galaxy field based on a convolutional neural network. We constrain H₀ and S₈ 1.5 and 1.9 times more tightly than power spectrum analyses. With this increased precision, our constraints are competitive with those of other cosmological probes, even with only 10% of the full BOSS volume. Future work extending SimBIG to upcoming spectroscopic galaxy surveys (DESI, PFS, Euclid) will produce improved cosmological constraints that will develop understanding of cosmic tensions.

标准Λ CDM宇宙学模型预测冷暗物质的存在，当前宇宙在暗能量的驱动下加速膨胀。由于使用H ₀和S ₈参数化的宇宙膨胀和生长历史测量中的张力，该模型最近受到审查。星系的三维聚类编码了解决这些紧张局势的关键宇宙学信息。在这里，我们使用基于模拟的推理提出了一组宇宙学约束，该推理利用了星系聚类中非线性尺度上的额外非高斯信息，而当前的分析无法访问这些信息。我们使用 SimBIG 分析重子振荡光谱巡天 (BOSS) 星系巡天的子集， SimBIG是一种新的宇宙学推理框架，利用高保真模拟和深度生成模型。我们使用超出标准功率谱的两种聚类统计数据：双谱和基于卷积神经网络的星系场摘要。我们对H ₀和S _{8 的}约束比功率谱分析严格1.5 倍和1.9 倍。随着精度的提高，我们的约束与其他宇宙学探测器的约束具有竞争力，即使只有整个 BOSS 体积的 10%。未来将SimBIG扩展到即将进行的光谱星系巡天（DESI、PFS、Euclid）的工作将产生改进的宇宙学约束，从而加深对宇宙张力的理解。