Baryonic Acoustic Oscillations in Large-Scale Structures

即将到来的暗能量光谱仪(DESI)将测量众多星系的位置，并测量暗能量对宇宙膨胀的影响。通过估计宇宙中星系团的功率谱单极子，我可以推断出关于时空几何的信息。从后者出发，我将测量DESI合作的星系团数据功率谱中的重子声振荡。这将使我能够通过应用先进的统计方法，如蒙特卡洛马尔科夫链模型，来推断宇宙参数的约束条件。分离沿视线和垂直于视线的分量的功率谱测量，我将能够测量具有红移空间失真和重子声振荡信号的阿尔科克-帕金斯基测试。Alcock-Paczynski测试描述了星系团中其他各向同性特征的扭曲，当时用于将测量的红移传递到距离的假设基准宇宙学模型偏离了真正的宇宙学。这种各向异性信号可能随着无特征功率谱中的红移空间失真信号而出现退化。使用重子声振荡信号，将有可能打破这种简并性，并利用所有三种信号，红移空间失真，重子声振荡和阿尔科克-帕金斯基效应来限制宇宙学参数。目前，我正在研究一种准确而稳健的功率谱估计器，用于模拟和真实数据。在不久的将来，我们将随后在模型中添加额外的特征，以提取BAO信号。

The upcoming Dark Energy Spectroscopic Instrument (DESI) will measure the positions of numerous galaxies and measure the effect of dark energy on the expansion of the universe. By estimating the power spectrum monopole from the galaxy clustering in the Universe I can deduce information about the geometry of space-time. From the latter I will measure the baryon acoustic oscillations in the power spectrum of the galaxy clustering data of the DESI collaboration. This will allow me, by applying advanced statistical methods such as Monte Carlo Markov Chain models, to deduce constraints for cosmological parameters. Separating the measurement of the power spectrum in a component along the line of sight and perpendicular to it, I will be able to measure the Alcock-Paczynski test with both the redshift space distortion and baryon acoustic oscillations signals. The Alcock-Paczynski test describes a distortion in an otherwise isotropic feature in the galaxy clustering when the assumed fiducial cosmological model used to transfer the measured redshifts into distances deviates from the true cosmology. This anisotropic signal may appear degenerate with the redshift space distortion signal in a featureless power spectrum. Using the baryon acoustic oscillations signal, it will be possible to break this degeneracy and exploit all three signals, redshift space distortion, baryon acoustic oscillations and the Alcock-Paczynski effect for cosmological parameter constraints. Currently, I am working on an accurate and robust power spectrum estimator for simulated and real data. In the near future, we will subsequently add additional features to the model in order to extract the BAO signal.