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arXiv 2412.09024astro-ph.COgr-qc
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相互作用暗能量模型

Interacting dark energy models

Robert Rugg, Shambel Sahlu, Amare Abebe

AI总结:

本研究针对两种暗物质-暗能量线性相互作用模型,用MCMC结合Pantheon与OHD(BAO)数据集约束,发现不同数据集结果存在差异,相关结果可用于探讨标准宇宙学的巧合等问题。

摘要:

本工作聚焦于暗物质与暗能量之间的两种线性相互作用模型,这类模型被提出是解释宇宙演化历史——尤其是宇宙晚期加速膨胀——的关键因素。研究采用马尔可夫链蒙特卡洛分析(MCMC),结合不同的观测数据集对两种模型进行约束。分析使用了Pantheon数据集(包含来自Pantheon分析的1048个Ia型超新星(SNIa)距离模数测量点),以及采用重子声学振荡(BAO)的观测哈勃参数(OHD)数据集(包含57个距离和膨胀率测量数据点)。两种模型在OHD(BAO)数据下均得到了合理结果:相互作用分别依赖暗物质和暗能量的两种模型,对应的暗物质占比更高,分别为56%和44%,哈勃参数分别为65.7±3 km/s/Mpc和65.8±3 km/s/Mpc。然而Pantheon数据集预测两种模型均存在反向相互作用,与最初的假设不符;该数据集测得的暗物质占比为18%和20%,哈勃参数分别为72.1±0.003 km/s/Mpc和72.3±0.004 km/s/Mpc。约束结果被用于重新探讨标准宇宙学中的巧合问题及其他相关问题。本次分析显示不同数据集的结果存在差异,其中一个数据集的误差范围较大。

英文摘要:

This work focuses on two linear interaction models between dark matter and dark energy, which are proposed as key factors in explaining cosmic history, specifically the latetime accelerating expansion of the universe. Both models are constrained using a Markov chain Monte Carlo analysis (MCMC) using different sets of observational data. The analysis was composed using the Pantheon data set, consisting of 1048 points of SNIa distance moduli measurements from the Pantheon analysis and the Observed Hubble Parameter (OHD) data set using Baryon acoustic Oscillation (BAO), consisting of 57 data points using distance and expansion rate measurement. Both models showed promising results with the OHD data (BAO), with a interaction that results in a higher dark matter content of 56% and 44%, and a Hubble parameter of 65.7+-3km/ s/Mpc and 65.8+-3km/ s/Mpc for the interaction dependent on dark matter and dark energy respectively. The pantheon data set however predicted a reverse interaction for both models which does not follow initial assumptions that were made. The pantheon data measured a dark matter content of 18% and 20% with a Hubble parameter of 72.1 +- 0.003km/ s/Mpc and 72.3 +- 0.004km /s/Mpc. The constrained results are used to revisit the coincidence problem and other problems in standard cosmology. The analysis provided a discrepancy between the different data sets with one having a large error margin.

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。

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