论文标题
测试特殊速度的贝叶斯重建方法
Testing Bayesian reconstruction methods from peculiar velocities
论文作者
论文摘要
从星系距离调查中重建大规模密度和速度场是宇宙学的主要挑战。数据非常嘈杂且稀疏。估计的距离（因此速度）受到毫米样的对数正态偏置的强烈影响。最近引入了两种算法，以从此类数据中进行重建：偏置高斯校正与Wiener Filter（BGC/WF）以及汉密尔顿蒙特卡洛（Monte Carlo）正向建模的小村庄实施。此处对这两种方法进行了测试，以模仿Cosmicflows-3数据的模拟目录。特别是检查了速度场（单子，偶极子）的重建磁当术和矩。还对``精确''维也纳滤镜进行了比较 - 即在零观察误差的不切实际情况下，维纳尔过滤器。这是为了了解WF方法的限制。找到以下内容。在附近的制度（$ d \ lyssim 40 {\ rm mpc}/h $）中，这两种方法的性能大致相同。 Hamlet在中级制度中的表现稍好（$ 40 \ Lessim D \ Lessim 120 {\ rm MPC}/H $）。两者之间的主要区别出现在最遥远的制度（$ d \ gtrsim 120 {\ rm mpc}/h $），靠近数据的边缘。小村庄的表现优于BGC/WF，而较紧密的相关性，但在遥远的政权中，小村庄会产生有些偏见的重建。 BGC/WF重建中缺少此类偏见。总而言之，两种方法都表现良好，并在检查细节时会出现明显差异的可靠重建。
Reconstructing the large scale density and velocity fields from surveys of galaxy distances, is a major challenge for cosmography. The data is very noisy and sparse. Estimated distances, and thereby peculiar velocities, are strongly affected by the Malmquist-like lognormal bias. Two algorithms have been recently introduced to perform reconstructions from such data: the Bias Gaussian correction coupled with the Wiener filter (BGc/WF) and the HAMLET implementation of the Hamiltonian Monte Carlo forward modelling. The two methods are tested here against mock catalogs that mimic the Cosmicflows-3 data. Specifically the reconstructed cosmography and moments of the velocity field (monopole, dipole) are examined. A comparison is made to the ``exact'' wiener filter as well - namely the Wiener Filter in the unrealistic case of zero observational errors. This is to understand the limits of the WF method. The following is found. In the nearby regime ($d \lesssim 40 {\rm Mpc}/h$) the two methods perform roughly equally well. HAMLET does slightly better in the intermediate regime ($ 40 \lesssim d \lesssim 120 {\rm Mpc}/h$). The main differences between the two appear in the most distant regime ($d \gtrsim 120 {\rm Mpc}/h$), close to the edge of the data. The HAMLET outperforms the BGc/WF in terms of better and tighter correlations, yet in the distant regime the HAMLET yields a somewhat biased reconstruction. Such biases are missing from the BGc/WF reconstruction. In sum, both methods perform well and create reliable reconstructions with significant differences apparent when details are examined.
