半对称度量引力：简要综述
Semi-Symmetric Metric Gravity: A Brief Overview
AI总结：
本文综述半对称度量引力（SSMG）理论，其基于1924年联络扩展广义相对论，引入挠率矢量，可望解决奇点、暴胀及锂问题，并回顾宇宙学应用与观测约束。
摘要：
我们综述了半对称度量引力（SSMG）理论，该理论是基于Friedmann和Schouten于1924年引入的联络，对标准广义相对论的一种几何扩展。半对称联络是Levi-Civita联络的推广，它允许存在一种简单形式的挠率，该挠率由挠率矢量描述。爱因斯坦场方程被假定为与标准广义相对论具有相同形式，从而将通过半对称联络构造的爱因斯坦张量与能量-动量张量联系起来。在场方程中包含挠率贡献具有引人入胜的宇宙学意义，特别是在宇宙晚期演化过程中。据推测，这些效应在高能条件下也占主导地位，因此SSMG可能解决广义相对论和宇宙学中的未决问题，如初始奇点、暴胀或大爆炸核合成中的$^7$Li问题。场方程中挠率的显式存在导致能量-动量张量不守恒，这可以在开放系统的不可逆热力学框架内解释为描述粒子产生过程。我们还详细回顾了该理论的宇宙学应用，并通过与多个观测数据集的比较来约束模型参数，对若干模型进行了统计检验。
英文摘要：
We present a review of the Semi-Symmetric Metric Gravity (SSMG) theory, representing a geometric extension of standard general relativity, based on a connection introduced by Friedmann and Schouten in 1924. The semi-symmetric connection is a connection that generalizes the Levi-Civita one, by allowing for the presence of a simple form of the torsion, described in terms of a torsion vector. The Einstein field equations are postulated to have the same form as in standard general relativity, thus relating the Einstein tensor constructed with the help of the semi-symmetric connection, with the energy-momentum tensor. The inclusion of the torsion contributions in the field equations has intriguing cosmological implications, particularly during the late-time evolution of the Universe. Presumably, these effects also dominate under high-energy conditions, and thus SSMG could potentially address unresolved issues in General Relativity and cosmology, such as the initial singularity, inflation, or the $^7$Li problem of the Big-Bang Nucleosynthesis. The explicit presence of torsion in the field equations leads to the non-conservation of the energy-momentum tensor, which can be interpreted within the irreversible thermodynamics of open systems, as describing particle creation processes. We also review in detail the cosmological applications of the theory, and investigate the statistical tests for several models, by constraining the model parameters via comparison with several observational datasets.
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