Multiparameter linear least-squares fitting to Poisson data one count at a time

Details Multiparameter linear least-squares fitting to Poisson data one count at a time Multiparameter linear least-squares fitting to Poisson data one count at a time

Jan 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995apj...438..322w&link_type=abstract

Astrophysical Journal, Part 1 (ISSN 0004-637X), vol. 438, no. 1, p. 322-340

Astronomy and Astrophysics

Astronomy

24

Flux Density, Gamma Ray Astronomy, Least Squares Method, Linear Equations, Poisson Equation, Correlation, Cosmic Rays, Data Processing, Gamma Ray Spectrometers

Scientific paper

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multicomponent linear model, with underlying physical count rates or fluxes which are to be estimated from the data. Despite its conceptual simplicity, the linear least-squares (LLSQ) method for solving this problem has generally been limited to situations in which the number ni of counts in each bin i is not too small, conventionally more than 5-30. It seems to be widely believed that the failure of the LLSQ method for small counts is due to the failure of the Poisson distribution to be even approximately normal for small numbers. The cause is more accurately the strong anticorrelation between the data and the wieghts wi in the weighted LLSQ method when square root of ni instead of square root of bar-ni is used to approximate the uncertainties, sigmai, in the data, where bar-ni = E(ni), the expected value of Ni. We show in an appendix that, avoiding this approximation, the correct equations for the Poisson LLSQ (PLLSQ) problems are actually identical to those for the maximum likelihood estimate using the exact Poisson distribution. We apply the method to solve a problem in high-resolution gamma-ray spectroscopy for the JPL High-Resolution Gamma-Ray Spectrometer flown on HEAO 3. Systematic error in subtracting the strong, highly variable background encountered in the low-energy gamma-ray region can be significantly reduced by closely pairing source and background data in short segments. Significant results can be built up by weighted averaging of the net fluxes obtained from the subtraction of many individual source/background pairs. Extension of the approach to complex situations, with multiple cosmic sources and realistic background parameterizations, requires a means of efficiently fitting to data from single scans in the narrow (approximately = 1.2 keV, HEAO 3) energy channels of a Ge spectrometer, where the expected number of counts obtained per scan may be very low. Such an analysis system is discussed and compared to the method previously used.

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