University of Chicago

- Review Home
- Introduction
- Observables
- Acoustic Peaks
- Matter Power Spectrum
- Gravitational Secondaries
- Scattering Secondaries
- Non-Gaussianity
- Data Analysis
- Discussion

An experiment can be characterized by the data taken at many different times; a *pointing
matrix* , relating the data timestream to the underlying signal at pixelized
positions indexed by , and a noise matrix characterizing the covariance of the noise in the timestream.
A model for the data then is (with implicit sum over the repeating index
); it is the sum of signal plus noise. Here is drawn from a distribution (often Gaussian) with mean zero and
covariance . In its simplest form the pointing
matrix contains rows - which corresponds to a particular time - with
all zeroes in it except for one column with a one (see Figure 5).
That column corresponds to the particular pixel observed at the time of interest.
Typically, a pixel will be scanned many times during an experiment, so a given
column will have many ones in it, corresponding to the many times the pixel
has been observed.

Given this model, a well-posed question is: what is the optimal estimator
for the signal ? i.e. what is the best way to construct a map? The answer stems
from the likelihood function , defined as the probability of getting the data given the theory
. In this case, the *theory* is the set
of parameters ,

That is, the noise, the difference between the data and the modulated signal, is assumed to be Gaussian with covariance .

There are two important theorems useful in the construction of a map and more
generally in each step of the data pipeline [Tegmark et al, 1997]. The first is *Bayes'
Theorem*. In this context, it says that , the probability that the temperatures are equal
to given the data, is proportional to the likelihood function
times a *prior* . Thus, with a uniform prior,

(27) |

with the normalization constant determined by requiring the integral of the probability
over all to be equal to one. The probability on the left is the one
of interest. The most likely values of therefore are those which maximize the likelihood function.
Since the log of the likelihood function in question, Equation (26),
is quadratic in the parameters , it is straightforward to find this maximum point. Differentiating
the argument of the exponential with respect to and setting to zero leads immediately to the estimator

where . As the notation suggests, the mean of the estimator is equal to the actual and the variance is equal to .

The second theorem states that this maximum likelihood estimator is also the
minimum variance estimator. The *Cramer-Rao*
inequality says no estimator can measure the with errors smaller than the diagonal elements of , where the *Fisher matrix*
is defined as

Inspection of Equation (26) shows that, in
this case the Fisher matrix is precisely equal to . Therefore, the Cramer-Rao theorem implies that the estimator
of Equation (28) is optimal: it has the smallest
possible variance [Tegmark, 1997a]. No information is lost if the
map is used in subsequent analysis instead of the timestream data, but huge factors
of compression have been gained. For example, in the recent Boomerang experiment
[Netterfield et al, 2001], the timestream contained
numbers, while the map had *only* pixels. The map resulted in compression by a factor of .

There are numerous complications that must be dealt with in realistic applications
of Equation (28). Perhaps the most difficult
is estimation of , the timestream noise covariance.
This typically must be done from the data itself [Ferreira & Jaffe, 2000,Stompor et al, 2001]. Even if were known perfectly, evaluation of the map involves inverting
, a process which scales as the number of raw data points cubed.
For both of these problems, the assumed *stationarity*
of (it depends only on ) is of considerable utility. Iterative techniques to approximate
matrix inversion can also assist in this process [Wright et al, 1996]. Another issue which
has received much attention is the choice of pixelization. The community has
converged on the Healpix pixelization scheme^{},
now freely available.

Perhaps the most dangerous complication arises from astrophysical foregrounds, both within and from outside the Galaxy, the main ones being synchrotron, bremmsstrahlung, dust and point source emission. All of the main foregrounds have different spectral shapes than the blackbody shape of the CMB. Modern experiments typically observe at several different frequencies, so a well-posed question is: how can we best extract the CMB signal from the different frequency channels [Bouchet & Gispert, 1999]? The blackbody shape of the CMB relates the signal in all the channels, leaving one free parameter. Similarly, if the foreground shapes are known, each foreground comes with just one free parameter per pixel. A likelihood function for the data can again be written down and the best estimator for the CMB amplitude determined analytically. While in the absence of foregrounds, one would extract the CMB signal by weighting the frequency channels according to inverse noise, when foregrounds are present, the optimal combination of different frequency maps is a more clever weighting that subtracts out the foreground contribution [Dodelson, 1997]. One can do better if the pixel-to-pixel correlations of the foregrounds can also be modeled from power spectra [Tegmark & Efstathiou, 1996] or templates derived from external data.

This picture is complicated somewhat because the foreground shapes are not precisely known, varying across the sky, e.g. from a spatially varying dust temperature. This too can be modelled in the covariance and addressed in the likelihood [Tegmark, 1998,White, 1998]. The resulting cleaned CMB map is obviously noisier than if foregrounds were not around, but the multiple channels keep the degradation managable. For example, the errors on some cosmological parameters coming from Planck may degrade by almost a factor of ten as compared with the no-foreground case. However, many errors will not degrade at all, and even the degraded parameters will still be determined with unprecedented precision [Knox, 1999,Prunet et al, 2000,Tegmark et al, 2000].

Many foregrounds tend to be highly non-Gaussian and in particular well-localized in particular regions of the map. These pixels can be removed from the map as was done for the region around the galactic disk for COBE. This technique can also be highly effective against point sources. Indeed, even if there is only one frequency channel, external foreground templates set the form of the additional contributions to , which, when properly included, immunize the remaining operations in the data pipeline to such contaminants [Bond et al, 1998]. The same technique can be used with templates of residual systematics or constraints imposed on the data, from e.g. the removal of a dipole.