Proceedings of the Particle Physics and Early Universe Conference (PPEUC).
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2 Component separation methods  

As a first step in discussing component separation methods, let us consider in more detail how the simulated data are made. At any given frequency the total rms temperature fluctuation on the sky in a direction is given by the superposition of physical components ( in our simulations). It is convenient to factorise the contribution of each process into a spatial template at a reference frequency and a frequency dependence , so that

In this paper we take the reference frequency and normalise the frequency dependencies such that .

If we observe the sky at observing frequencies then, in any given direction , we obtain a -component data vector that contains the observed temperature fluctuation in this direction at each observing frequency plus instrumental noise. In order to relate this data vector to the emission from each physical component it is useful to introduce the frequency response matrix with components defined by


where is the frequency response (or transmission) of the th frequency channel. Assuming that the satellite observing beam in each channel is spatially invariant, we may write the beam-smoothing as a convolution and, in discretised form, the th component of the data vector in the direction is then given by


where is the beam profile for the th frequency channel, and the index labels the pixels in each of the simulated input maps; the term represents the instrumental noise in the th channel in the direction .

In each channel the beam profile is assumed spatially invariant and the noise statistically homogeneous (which are both reasonable assumptions for small fields), and it is more convenient to work in Fourier space, since the convolution in (2) becomes a simple multiplication and we obtain


where are the components of the response matrix for the observations. It is important to note that (3) is satisfied at each Fourier mode independently. Thus, in matrix notation, at each mode we have


where , and are column vectors containing , and complex components respectively, and the response matrix and has dimensions . Although the column vectors in (4) refer to quantities defined in the Fourier domain, it should be noted that for later convenience they are not written with a tilde.

The significant simplification that results from working in the Fourier domain is clear, since the dimensions of the matrices in (4) are rather small ( and in our simulations). Thus, the situation reduces to the solving a small-scale linear inversion problem at each Fourier mode separately. Once this inversion has been performed for all the measured modes, the spatial templates for the sky emission due to each physical component at the reference frequency are then obtained by an inverse Fourier transformation. Due to the presence of instrumental noise, however, it is clear that the inverse, , of the response matrix at each Fourier mode does not exist and that the linear inversion problem in each case is degenerate. The approximate inversion of (4) must therefore be performed using a statistical technique in which the inversion is regularised in some way. This naturally leads us to consider a Bayesian approach.

2.1 Bayes' theorem  

Bayes' theorem states that, given a hypothesis and some data the posterior probability is the product of the likelihood and the prior probability , normalised by the evidence ,

In our application, we consider each Fourier mode separately and, from (4), we see that the data consist of the complex numbers in the data vector , and we take the `hypothesis' to consist of the complex numbers in the signal vector . We then choose as our estimator of the signal vector that which maximises the posterior probability . Since the evidence in Bayes' theorem is merely a normalisation constant we must therefore maximise with respect to the quantity


which is the product of the likelihood and the prior .

Let us first consider the form of the likelihood. If the instrumental noise on each frequency channel is Gaussian-distributed, then at each Fourier mode the probability distribution of the -component noise vector is described by an -dimensional multivariate Gaussian. Assuming the expectation value of the noise to be zero at each observing frequency, the likelihood is therefore given by


where the dagger denotes the Hermitian conjugate and in the second line we have used (4). We note that no factor of appears in the exponent in (6) since it refers to the multivariate Gaussian distribution of a set of complex random variables. The noise covariance matrix has dimensions and at any given Fourier mode it is defined by


i.e. its elements are given by , where the asterisk denotes complex conjugation. Thus, at a given Fourier mode, the th diagonal element of contains the value at that mode of the ensemble-averaged power spectra of the instrumental noise on the th frequency channel. If the noise is uncorrelated between channels then the off-diagonal elements are zero for all .

We note that the expression in square brackets in (6) is simply the misfit statistic. Since, for a given set of observations, the data vector , the response matrix and the noise covariance matrix are all fixed, we may consider the misfit statistic as a function only of the signal vector ,


so that the likelihood can be written as


Having calculated the form of the likelihood we must now turn our attention to the form of the prior probability .

2.2 The Gaussian prior  

If we assume that the emission due to each of the physical components is well approximated by a Gaussian random field, then it is straightforward to derive an appropriate form for the prior . In this case, the probability distribution of the sky emission is described by a multivariate Gaussian distribution, characterised by a given sky covariance matrix. Thus, at each mode in Fourier space, the probability distribution of the signal vector is also described by a multivariate Gaussian of dimension , where is the number of distinct physical components ( in our simulations). The prior therefore has the form


where the signal covariance matrix is real with dimensions and is given by


i.e. it has elements . Thus, at each Fourier mode, the th diagonal element of contains the value of the ensemble-averaged power spectrum of the th physical component at the reference frequency ; the off-diagonal terms describe cross-power spectra between the components.

Strictly speaking, the use of this prior requires advance knowledge of the full covariance structure of the processes that we are trying to reconstruct. Nevertheless, it is anticipated that some information concerning the power spectra of the various components, and correlations between them, will be available either from pre-existing observations or by performing an initial approximate separation using, for example, singular value decomposition (see Bouchet et al. (1997)). This information can then be used to construct an approximate signal covariance matrix for use in .

Substituting (9) and (10) into (5), the posterior probability is then given by


where is given by (8). Completing the square for in the exponential (see Zaroubi (1995)), it is straightforward to show that the posterior probability is also a multivariate Gaussian of the form


which has its maximum value at the estimate of the signal vector and where is the covariance matrix of the reconstruction errors.

The estimate of the signal vector is found to be


where we have identified the Wiener matrix . Thus, we find that by assuming a Gaussian prior of the form (10) in Bayes' theorem, we recover the standard Wiener filter. This optimal linear filter is usually derived by choosing the elements of such that they minimise the variances of the resulting reconstruction errors. From (14) we see that at a given Fourier mode, we may calculate the estimator that maximises the posterior probability simply by multiplying the data vector by the Wiener matrix . Equation (14) can also be derived straightforwardly by differentiating (12) with respect and equating the result to zero (see Appendix A).

As is well-known, the assignment of errors on the Wiener filter reconstruction is straightforward and the covariance matrix of the reconstruction errors in (14) is given by


Since the posterior probability (13) is Gaussian, this matrix is simply the inverse Hessian matrix of (minus) the exponent in (13), evaluated at (see Appendix A).

It should be noted that the linear nature of the Wiener filter and the simple propagation of errors are both direct consequences of assuming that the spatial templates we wish to reconstruct are well-described by Gaussian random fields with known covariance structure.

2.3 The entropic prior  

The emission due to several of the underlying physical processes may be far from Gaussian. This is particularly pronounced for kinetic and thermal SZ effects, but Galactic dust and free--free emissions also appear quite non-Gaussian. Ideally, one might like to assign priors for the various physical components by measuring empirically the probability distribution of temperature fluctuations from numerous realisations of each component. This is not feasible in practice, however, and instead we consider here the use of the entropic prior, which is based on information-theoretic considerations alone.

Let us consider a discretised image consisting of cells, so that ; we may consider the as the components of an image vector . Using very general notions of subset independence, coordinate invariance and system independence, it may be shown Skilling (1989) that the prior probability assigned to the values of the components in this vector should take the form

where the dimensional constant depends on the scaling of the problem and may be considered as a regularising parameter, and is a model vector to which defaults in the absence of any data. The function is the cross entropy of and . In standard applications of the maximum entropy method, the image is taken to be a positive additive distribution (PAD). Nevertheless, the MEM approach can be extended to images that take both positive and negative values by considering them to be the difference of two PADS, so that

where and are the positive and negative parts of respectively. In this case, the cross entropy is given by (Gull & Skilling (1990); Maisinger, Hobson & Lasenby (1997))


where and and are separate models for each PAD. The global maximum of the cross entropy occurs at .

In our application, we might initially suppose that at each Fourier mode we should take the `image' to be the components of the signal vector . However, this results in two additional complications. First, the components of signal vector are, in general, complex, but the cross entropy given in (16) is defined only if the image is real. Nevertheless, the MEM technique can be straightforwardly extended to the reconstruction of a complex image by making a slight modification to the above discussion. If the image is complex, then models and are also taken to be complex. In this case, the real and imaginary parts of are the models for the positive portions of the real and imaginary parts of respectively. Similarly, the real and imaginary parts of are the models for the negative portions of the real and imaginary parts of the image. The total cross entropy is then obtained by evaluating the sum (16) using first the real parts and then the imaginary parts of , and , and adding the results. Thus the total cross entropy for the complex image is given by


where and denote the real and imaginary parts of each vector. For simplicity we denote the sum (17) by where the subscript indicates that it is the entropy of a complex image.

The second complication mentioned above is more subtle and results from the fact that one of the fundamental axioms of the MEM is that it should not itself introduce correlations between individual elements of the image. However, as discussed in previous subsection, the elements of the signal vector at each Fourier mode may well be correlated, this correlation being described by the signal covariance matrix defined in (11). Moreover, if prior information is available concerning these correlations, we would wish to include it in our analysis. We are therefore lead to consider the introduction of an intrinsic correlation function (ICF) into the MEM framework (Gull & Skilling (1990)).

The inclusion of an ICF is most easily achieved by assuming that, at each Fourier mode, the `image' does not consist of the components of the signal vector , but that instead consists of the components of a vector of hidden variables that are related to the signal vector by


The lower triangular matrix in (18) is that obtained by performing a Cholesky decomposition of the signal covariance matrix, i.e. . We note that since is real then so is . Thus, if the components of are apriori uncorrelated (thereby satisfying the MEM axiom) and of unit variance, so that , we find that, as required, the a priori covariance structure of the signal vector is given by

Moreover, using this construction the expected rms level for the real or imaginary part of each element of is simply equal to . Therefore, at each Fourier mode, we assign the real and imaginary parts of every component in the model vectors and to be equal to .

Substituting (18) into (8), can also be written in terms of and is given by


Thus, using an entropic prior, the posterior probability becomes


where the cross entropy is given by (17) and (16).

2.4 Maximising the posterior probability  

As discussed in Section 2.1, we choose our estimate of the signal vector at each Fourier mode, as that which maximises the posterior probability with respect to .

For the Gaussian prior, we found in Section 2.2 that the posterior probability is also a Gaussian and that the estimate is given directly by the linear relation (14). Nevertheless, we also note that, in terms of defined in (18), the quadratic form in the exponent of the Gaussian prior (10) has the particularly simple form

i.e. it is equal the inner product of with itself. Thus, using a Gaussian prior, the posterior probability can be written in terms of as


where is given by (19) Therefore, in addition to using the linear relation (14), the Wiener filter estimate can also be found by first minimising the function


to obtain the estimate of the corresponding hidden vector, and then using (18) to give .

We have developed an algorithm (which will be presented in a forthcoming paper) for minimising the function with respect to . Indeed, this algorithm calculates the reconstruction in slightly less CPU time than the matrix inversions and multiplications required to evaluate the linear relation (14). The minimiser requires only the first derivatives of the function and these are given in Appendix A.

Let us now consider the MEM solution. From (20), we see that maximising the posterior probability when assuming an entropic prior is equivalent to minimising the function


The minimisation of this -dimensional functions may also performed using the minimisation algorithm mentioned above, and the required first derivatives in this case are also given in Appendix A.

It is important to note that, since we are using the same minimiser to obtain both the Wiener filter (WF) and MEM reconstructions, and the evaluation of each function and its derivatives requires similar amounts of computation, the two methods require approximately the same CPU time. Thus, at least in this application, any criticism of MEM that is based on its greater computational complexity, as compared to the WF, is no longer valid. For both the WF and the MEM, the reconstruction of the six maps of the input components requires only about two minutes on a Sparc Ultra workstation.

2.5 The small fluctuation limit  

Despite the formal differences between (22) and (23), the WF and MEM approaches are closely related. Indeed the WF can be viewed as a quadratic approximation to MEM, and is commonly referred to as such in the literature. This approximation is most easily verified by considering the small fluctuation limit, in which the real and imaginary parts of are small compared to the corresponding models.

Following the discussion at the end of Section 2.3, we begin by setting the real and imaginary parts of all the components of the models vectors and equal to . Then, expanding the sum in (16) as a power series in and using (17), we find that for small the total cross entropy is approximated by


Thus, in the small fluctuation limit, the posterior probability assuming an entropic prior becomes Gaussian and is given


In fact, this approximation is reasonably accurate provided the magnitudes of the real and imaginary parts of each element of are less than about . Since is set equal to the expected rms of level these parameters, we would therefore expect that for a Gaussian process this approximation should remain valid. In this case, the posterior probability (25) becomes identical to that for the WF solution, provided we set .

We note, however, that for highly non-Gaussian processes, the magnitudes of the real and imaginary parts of the elements of can easily exceed and in this case the shapes of the posterior probability for the WF and MEM approaches become increasingly different.

2.6 The regularisation constant   

A common criticism of MEM has been the arbitrary choice of regularisation constant , which is often considered merely as a Lagrange multiplier. In early applications of MEM, was chosen so that the misfit statistic equalled its expectation value, i.e. the number of data points to be fitted. This choice is usually referred to as historic MEM.

In the reconstruction of Fourier modes presented here, the situation is eased somewhat since the choice is at least guaranteed to reproduce the results of the Wiener filter when applied to Gaussian processes. In fact, when applied to the simulations presented in the companion paper, this choice of does indeed bring into its expected statistical range , where is the number of (complex) values in the data vector at each Fourier mode.

Nevertheless, it is possible to determine the appropriate value for in a fully Bayesian manner (Skilling (1989); Gull & Skilling (1990)) by simply treating it as another parameter in our hypothesis space. It may be shown (see Appendix B) that must be a solution of


where is the hidden vector that maximises the posterior probability for this value of . The matrix is given by

where is the Hessian matrix of the function at the point and is the metric on image-space at this point.

It should be noted that both the reconstruction and the matrix depend on and so (26) must be solved numerically using an iterative technique such as linear interpolation or the Newton--Raphson method. We take as our initial estimate in order to coincide with the Wiener filter in the small fluctuation limit. For any particular value of , the corresponding reconstruction is obtained by minimising as given in (23), and the Hessian of the posterior probability at this point is then calculated (see Appendix A). This in turn allows the evaluation of and respectively. Typically, fewer than ten iterations are needed in order to converge on a solution that satisfies (26).

2.7 Updating the ICF and models  

In the MEM approach, after the Bayesian value for the regularisation constant has been found, the corresponding posterior probability distribution is maximised to obtain the reconstruction , from which the estimate of the signal vector may be straightforwardly derived. Once this has been performed for each Fourier mode, the reconstruction of the sky emission due to each physical component is then found by performing an inverse Fourier transform.

We could, of course, end our analysis at this point and use the maps obtained as our final reconstructions. However, we find that the results can be further improved by using the current reconstruction to update the ICF matrix and the models and , and then repeating the entire MEM analysis discussed above. At each Fourier mode, the updated models are taken directly from the current reconstruction and the updated ICF matrix is obtained by calculating a new signal covariance matrix from the current reconstruction and performing a Cholesky decomposition. These quantities are then used in the next iteration of the MEM and the process is repeated until it converges on a final reconstruction. Usually, fewer than ten such iterations are required in order to achieve convergence.

We might expect that a similar method may be used in the WF case, by repeatedly calculating an updated signal covariance matrix from the current reconstruction and using it in the subsequent iteration of the WF analysis. It is well-known, however, that, since the WF tends to suppress power at higher Fourier modes, the solution gradually tends to zero as more iterations are performed. This behaviour was indeed confirmed by experiment.

2.8 Estimating errors on the reconstruction  

Once the final reconstruction has been obtained, it is important to be able to characterise the errors associated with it. In the case of the Wiener filter, the reconstructed signal vector at each Fourier mode may be obtained in a linear manner from the observed data vector using (14). Thus the propagation of errors is straightforward and the covariance matrix of the reconstruction errors at each Fourier mode is given by (15).

As mentioned in Section 2.2, however, this simple propagation of errors is entirely a result of the assumption of a Gaussian prior, which, together with the assumption of Gaussian noise, leads to a Gaussian posterior probability distribution. In terms of the vector of hidden variables the posterior probability for the WF is given by

where the Hessian matrix is given by evaluated at the peak of the distribution, and the function is given by (22). Thus, the covariance matrix of the errors on the reconstructed hidden vector is then given exactly by the inverse of this matrix, i.e

From (18), the error covariance matrix for the reconstructed signal vector is then given by


Using the expression for the Hessian matrix given in (35), and remembering that and , the expression (27) is easily shown to be identical to the result (15).

For the entropic prior, the posterior probability distribution is not strictly Gaussian in shape. Nevertheless, we may still approximate the shape of this distribution by a Gaussian at its maximum and, recalling the discussion of subsection 2.5, we might expect this approximation to be reasonably accurate, particularly in the reconstruction of Gaussian processes. Thus, near the point , we make the approximation

where evaluated at , and is given by (23). The covariance matrix of the errors on the reconstructed hidden vector is then given approximately by the inverse of this matrix, and so

In both the WF and MEM cases, the reconstructed maps of the sky emission due to each physical component is obtained by inverse Fourier transformation of the signal vectors at each Fourier mode. Since this operation is linear, the errors on these maps may therefore be deduced straightforwardly from the above error covariance matrices.

PPEUC Proceedings
Tue Feb 24 09:25:54 GMT 1998