通过数据驱动方法区分宇宙学模型
Discriminating among cosmological models by data-driven methods
AI总结：
本文结合传统统计推断与机器学习分析 Pantheon+SH0ES 数据，评估六种暗能量模型，验证 $\Lambda$CDM 的稳健性，并发现 Boruta 特征选择可显著改善替代模型。
摘要：
我们探索 Pantheon+SH0ES 数据集，以识别能够区分不同宇宙学模型的模式。我们重点确定暗能量的行为是与标准 $\Lambda$CDM 模型一致，还是暗示了新的宇宙学特征。核心目标是评估 $\Lambda$CDM 模型相对于其他暗能量模型的稳健性，并研究是否存在可能指向新宇宙学洞见的偏差。该研究采用数据驱动方法，同时使用传统统计方法和机器学习技术。首先，我们利用 Markov Chain Monte Carlo（MCMC，马尔可夫链蒙特卡洛）、静态和动态嵌套抽样等传统统计方法评估六种不同的暗能量模型，以推断宇宙学参数。随后，我们采用机器学习方法，开发回归模型来计算每颗超新星的距离模数，并将特征集扩展到 74 个统计特征。传统统计分析证实 $\Lambda$CDM 模型是稳健的，给出了预期的参数值。其他模型表现出偏差，其中广义和修正 Chaplygin 气体模型表现较差。在机器学习分析中，特征选择技术（尤其是 Boruta）显著提升了模型性能。特别是，最初被认为较弱的模型（广义/修正 Chaplygin 气体）在特征选择后表现出显著改善。该研究证明了数据驱动方法在宇宙学模型评估中的有效性。$\Lambda$CDM 模型仍然稳健，而机器学习技术（尤其是特征选择）揭示了替代模型中的潜在改进，这些改进可能与近期 DESI 巡天等新的观测活动相关。
英文摘要：
We explores the Pantheon+SH0ES dataset to identify patterns that can discriminate between different cosmological models. We focus on determining whether the behaviour of dark energy is consistent with the standard $Λ$CDM model or suggests novel cosmological features. The central goal is to evaluate the robustness of the $Λ$CDM model compared with other dark energy models, and to investigate whether there are deviations that might indicate new cosmological insights. The study takes into account a data-driven approach, using both traditional statistical methods and machine learning techniques. Initially, we evaluate six different dark energy models using traditional statistical methods like Markov Chain Monte Carlo (MCMC), Static and Dynamic Nested Sampling to infer the cosmological parameters. Subsequently, we adopt a machine learning approach, developing a regression model to compute the distance modulus of each supernova, expanding the feature set to 74 statistical features. Traditional statistical analysis confirms that the $Λ$CDM model is robust, yielding expected parameter values. Other models show deviations, with the Generalised and Modified Chaplygin Gas models performing poorly. In the machine learning analysis, feature selection techniques, particularly Boruta, significantly improve model performance. In particular, models initially considered weak (Generalised/Modified Chaplygin Gas) show significant improvement after feature selection. The study demonstrates the effectiveness of a data-driven approach to cosmological model evaluation. The $Λ$CDM model remains robust, while machine learning techniques, in particular feature selection, reveal potential improvements in alternative models which could be relevant for new observational campaigns like the recent DESI survey.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
