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The next generation of CMB experiments can measure cosmological parameters with unprecedented accuracy - in principle. To achieve this in practice when faced with such gigantic data sets, elaborate data analysis methods are needed to make it computationally feasible. An important step in the data pipeline is to make a map, which typically reduces the size of the data set my orders of magnitude. We compare ten map-making methods, and find that for the Gaussian case, both the method used by the COBE DMR team and various variants of Wiener filtering are optimal in the sense that the map retains all cosmological information that was present in the time-ordered data (TOD). Specifically, one obtains just as small error bars on cosmological parameters when estimating them from the map as one could have obtained by estimating them directly from the TOD. The method of simply averaging the observations of each pixel (for total-power detectors), on the contrary, is found to generally destroy information, as does the maximum entropy method and most other non-linear map-making techniques. Since it is also numerically feasible, the COBE method is the natural choice for large data sets. Other lossless (e.g. Wiener-filtered) maps can then be computed directly from the COBE method map.

We present in this article two different ways to make CMB maps in practice, from large timelines. One is to make a simple destriping, fitting the data and using the scan intercepts to remove the low frequency noise (stripes). The second, optimal, is to resolve linearly the map-making problem, which in case of big timelines must be simplified and changed from matrices to vectors for the calculations. Assuming few conditions on the noise, it is possible to make fast map-making tools.

We present in this article two different ways to make CMB maps in practice, from large timelines. One is to make a simple destriping, fitting the data and using the scan intercepts to remove the low frequency noise (stripes). The second, optimal, is to resolve linearly the map-making problem, which in case of big timelines must be simplified and changed from matrices to vectors for the calculations. Assuming few conditions on the noise, it is possible to make fast map-making tools.

We present in this article two different ways to make CMB maps in practice, from large timelines. One is to make a simple destriping, fitting the data and using the scan intercepts to remove the low frequency noise (stripes). The second, optimal, is to resolve linearly the map-making problem, which in case of big timelines must be simplified and changed from matrices to vectors for the calculations. Assuming few conditions on the noise, it is possible to make fast map-making tools.

This PDF e-book is available free for non-commercial use. ------------------------------------------------------ Subject: How argument mapping can make discussions better (and shorter). Why different perceptions of reality can cause disagreement, and how diagrams can make people aware of this. Target audience: Everyone interested. No special knowledge required. Estimated reading time: 20-25 minutes Page layout: Allows easy reading without scrolling, even on very small screens. License: Free for non-commercial use, Creative Commons BY-NC-SA. Commercial use requires separate agreement. Technical information: Original PDF/A file size: 685 913 Byte MD5: 3abc45069b734d7d2a60b9331d55f33b (file integrity checksum)