论文标题
使用基于模拟的推理，从星系簇丰度中估算宇宙学的约束
Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference
论文作者
论文摘要
在宇宙学模型中推断宇宙学参数的价值和不确定性对于现代宇宙观察至关重要。在本文中，我们使用基于模拟的推理（SBI）方法来估计简化的星系群集观察分析的宇宙学约束。使用Quijote Simulation Suite和分析模型生成的数据，我们训练机器学习算法，以了解宇宙参数与可能的星系簇可观测值之间的概率函数。然后，通过从训练有素的算法中抽样预测来获得宇宙学参数的后验分布。我们的结果表明，对于此简化的星系群集分析，SBI方法可以成功恢复2σ极限内的宇宙学参数的真实值，并获得了使用基于可能的Markov Chain Monte Carlo方法获得的相似后验约束，在类似的宇宙学研究中使用了当前的ART方法。
Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a given observation is then obtained by sampling the predictions from the trained algorithm. Our results show that the SBI method can successfully recover the truth values of the cosmological parameters within the 2σ limit for this simplified galaxy cluster analysis, and acquires similar posterior constraints obtained with a likelihood-based Markov Chain Monte Carlo method, the current state-of the-art method used in similar cosmological studies.
