论文标题
霍华重力中动态暗能模型和宇宙张力的新测试
A New Test of Dynamical Dark Energy Models and Cosmic Tensions in Hořava Gravity
论文作者
论文摘要
Horava重力已被提议作为可重新分析的,更高衍生的洛伦兹侵略量子重力模型,而没有幽灵问题。早先提出了一种基于HORAVA重力的黑暗能量(HDE)模型,该模型通过确定洛伦兹侵入术语中的所有额外(重力)贡献作为爱因斯坦方程中的有效能量弹药张量。我们通过考虑对背景的一般扰动完美的HDE流体来考虑HDE模型的完整CMB,BAO和SNE IA数据测试。除了BAO外,我们还获得了所有其他数据集合组合的非平台宇宙的偏好。我们对哈勃常数H0和宇宙剪切S8之间的宇宙紧张局势获得了积极的结果,因为我们将H0转向更高的值,尽管不足以解决H0张力,但是S8的值未改变。这与H0相当降低,但在非弹性LCDM中增加了S8。对于所有其他参数,例如Omega_M和Omega_lambda,我们与LCDM的所有数据集获得了相当可比的结果,尤其是与BAO的结果,因此我们的结果与数据集之间的宇宙一致性接近,与标准的非FLAT LCDM相反。我们还获得了一些不希望的功能,例如Omegak上的几乎无效的结果,如果我们不确定Omegak的迹象,它会给Flat LCDM返回,但我们提出了几种有希望的改进方法来通过推广我们的分析。
Horava gravity has been proposed as a renormalizable, higher-derivative, Lorentz-violating quantum gravity model without ghost problems. A Horava gravity based dark energy (HDE) model for dynamical dark energy has been also proposed earlier by identifying all the extra (gravitational) contributions from the Lorentz-violating terms as an effective energy-momentum tensor in Einstein equation. We consider a complete CMB, BAO, and SNe Ia data test of the HDE model by considering general perturbations over the background perfect HDE fluid. Except from BAO, we obtain the preference of non-flat universes for all other data-set combinations. We obtain a positive result on the cosmic tensions between the Hubble constant H0 and the cosmic shear S8, because we have a shift of H0 towards a higher value, though not enough for resolving the H0 tension, but the value of S8 is unaltered. This is in contrast to a rather decreasing H0 but increasing S8 in a non-flat LCDM. For all other parameters, like Omega_m and Omega_Lambda, we obtain quite comparable results with those of LCDM for all data sets, especially with BAO, so that our results are close to a cosmic concordance between the datasets, contrary to the standard non-flat LCDM. We also obtain some undesirable features, like an almost null result on Omegak, which gives back the flat LCDM, if we do not predetermine the sign of Omegak, but we propose several promising ways for improvements by generalizing our analysis.