Astronomy and Astrophysics – Astrophysics
Scientific paper
2000-10-27
Mon.Not.Roy.Astron.Soc. 331 (2002) 901
Astronomy and Astrophysics
Astrophysics
Revised version with new section and figures. To appear in MNRAS
Scientific paper
10.1046/j.1365-8711.2002.05229.x
We present a new Unbiased Minimal Variance (UMV) estimator for the purpose of reconstructing the large--scale structure of the universe from noisy, sparse and incomplete data. Similar to the Wiener Filter (WF), the UMV estimator is derived by requiring the linear minimal variance solution given the data and an assumed prior model specifying the underlying field covariance matrix. However, unlike the WF, the minimization is carried out with the added constraint of an unbiased reconstructed mean field. The new estimator does not necessitate a noise model to estimate the underlying field; however, such a model is required for evaluating the errors at each point in space. The general application of the UMV estimator is to predict the values of the reconstructed field in un-sampled regions of space (e.g., interpolation in the unobserved Zone of Avoidance), and to dynamically transform from one measured field to another (e.g., inversion of radial peculiar velocities to over-densities). Here, we provide two very simple applications of the method. The first, is to recover a 1D signal from noisy, convolved data with gaps, e.g., CMB time-ordered data. The second application is a reconstruction of the density and 3D peculiar velocity fields from mock SEcat galaxy peculiar velocity catalogs.
No associations
LandOfFree
Unbiased Reconstruction of the Large Scale Structure does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Unbiased Reconstruction of the Large Scale Structure, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Unbiased Reconstruction of the Large Scale Structure will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-233810