宇宙大尺度结构和原初扰动非高斯性研究进展

摘要

宇宙微波背景辐射(CMB)探测和大尺度结构(LSS)巡天是两种研究宇宙演化的重要手段.近年来高精度的宇宙微波背景辐射实验和大尺度结构巡天可以对今天的宇宙学模型给出精确测量,比如宇宙曲率、暴胀模型参数和原初非高斯性等.宇宙大尺度结构的原初非高斯性揭示了在甚早期宇宙中引发大尺度结构的原初扰动的物理诱发机制,有效地对不同暴胀模型进行区分,是研究甚早期宇宙的有效工具和手段.但是最近的研究结果表明,很多大尺度结构巡天的数据被系统误差严重污染,如大气视宁度、天光背景等,这些误差导致由大尺度结构巡天数据给出的原初非高斯的研究结果与实际情况相差甚远.大尺度结构巡天数据的系统误差消除和它对限制宇宙原初非高斯性的改善,对更好地了解甚早期宇宙具有重要意义.%The cosmic microwave background radiation (CMBR) detection and the large scale structure (LSS) survey are two important tools to study on the evolution of the universe. The CMB experiment and the LSS detector could calculate and restrict on the cosmological parameters with very high precision, such as the curvature of the universe, the parameter of the inflation models and so on. The COBE satellite first showed the Non-Gaussianity among the observed CMB photons that in Gaussian distribution. The Primordial Non-Gaussianity (PNG) of the cosmic large scale structure explains the physical mechanism why there should exists a primordial perturbation and how it forms the large scale structure today in the very early universe. Also, it can help researcher to distinguish between different inflation models, and it is an effective tool for us to study very early universe. But recent research papers have shown that the large scale structure survey's data may be polluted badly by some systematic errors, such as seeing, airmass, sky brightness and so on, which would cause a big difference between the derived cosmic parameters and the actual counterparts. Therefore removing the systematic errors' effect for the LSS tracers on constraining primordial non-gaussianity is very meaningful so that we could get more real cosmic truth and know the early cosmology more clearly. In this paper, we will review the way how the CMB and LSS tracer's maps restrict on the cosmological parameters firstly and then we will disicuss about the systematic errors' effect on the observed angular power spectrum and also we introduce two important methods to remove the systematics. Finally, we will compare the modified parameter and the original one to discuss the goodness.