Least-Squares Deconvolution of Compton Telescope Data With the Positivity Constraint

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Least-Squares the

Wheaton”, David t, O. ‘1’urnerZycht*Jet Propulsion tlnstitute of Geophysics and Planetary

A13STRACTWe describe a I)econvolution Compton lrnposition of the additional physical constraint,that bc non-negative, has been found

et al. 1993) presents preliminary images of the Crab

Nebula region using data from Compion Gamma-Ray Observatory.

1.INTROD1JCTIONCompton gamma-ray

prob-]hown, 1986). (L1,SQ) have not generally been effective for Pois-son dcconvolution has mostly fallen to such nonlinear approaches as the maximum likelihoodand maximum entropy methods.Nevertheless, linear methods have many advantages that could make them attractiveJl ere wc describe a linear approach to the inversion of datafrom Compton telescopes that avoids some of the problems previously encountered. all sources tbc methodagainst ill-conditioning.

2.LEAST-SQUARES

(a, Wc bin the data space 1 = le x total bins, where il; = 1, ., .,0 by etc. Given ni is a sum with a term for eachsource:J

(1)

sing]c index 1 combiningie, i~). The elements Aij of the 3 matrix A (called the matrix), areconstants, known if the source positions and instrument response function are given.Fj ]incar transformation of the space Z. This linearity results from the fact

I this jn ~rgl,,ncl,t take of view and not occur physically. model and data spacesto positivity is considered in Section 3 ,“to take account of linear that such terms have been added as needed.In the model-fitting situation we J.LSQ algorithm (e.g.,al. 1971; Press flj of the fluxes

Aij are

functions of But for

it with a grid of sources (pixels)at known positions. If the pixels were placed at all possible positions (’distinguishablethe bc

solved by

cxpectcd signal. Strong Bcvington 1969,MODE= – (PI,LSQalgorithms) do not work reliably for small numbers of counts

di~culties arising from the size of the design matrix.Each of these maladies infecting dcconvolution is effectively curable: First,a method of multi-parameter data which works in the limitof y-ray spectroscopyand other purposes (Wheaton al. 1993). It is shown therein that weighted counts if the normalized weights xi wi = 1)for each equation ?li arc independent. Note that the /u~l/?~i violates this condition and must

rnodcl space. Finally, Singular Value I’ress aL 1986) is essential in practice nearly singular systems to be solved on dependenccs among the columns of A, without serious numerical precision problems.l)ircct Dcconvolution

problcm is in determining bc used, This latter obstacle has been

bc non-negative. This constraint dramatically improves the attainable resolution.

3.POSITIVITY CONSTRAINTThere are reasons to expect the

I’ositivity forces the negative excursions in the image to rise to zero, andit also forces adjacent positive excursions to anti-correlation. The result is Eqn. (1), Consider J = 9, no solution would Rutusing positivity, a row or column sum of O would imply all pixels were also zero: that is it would be equivalent to

ezpected counts (nl, . . . . iso-probability surfaces, J-dirncnsional ellipsoids, would (FI, . . . .cxpcctcd counts fii) >> 1, the distribution isvirtually normal. Because of the finite extent of the probability ellipsoid, the solutionfor any single bc outside of the positive region.a single gaussian trial, the likelihood function (e.g., bc peaked in c]lipsoid, similar to the onc dcscribcd above, butcentered at the point estimate, equation (2). positivity constraint excludes valuesoutside the first

estima.tc (2) is outside it; as it is, almost always, in

NN1,SWhile working toward an algorithm to find the above maximum, we discovered apre-existing subroutine which dots much of what wc need. FOR1’RAN subroutine(I,awson and 11 ] x aild ]-vector SOIVCSthe L=Ail(3)

for Xj NNI,S than previously. imagesof the Crab Nebula region with (l)ixon al. 1993) show that the spatialresolution obtained is essentially the instrumental resolution. Sources not

W11OSC total flux is essentially that of the single source peakseen at lower ut ion.For data of very high statistical quality, pixc]s in the image arc positive withoutconstraint. re-analyzing the data with a finer pixel grid, the oscillations willappear, and positivity can the ‘1’hus for high-statistics

data, the effect of positivity is to allow the effective resolution to be pressed beyondwhat would otherwise

J = 2 fits), when the net source estimatesare not significant so that many