COMPTEL skymapping: a new approach using parallel computing

TL;DR

This paper introduces a parallel computing approach for large-scale COMPTEL skymapping, utilizing a maximum-entropy algorithm to handle complex responses and variable backgrounds efficiently, demonstrated with 7 years of data.

Contribution

The paper presents a novel parallel computing method with a maximum-entropy algorithm for improved COMPTEL skymapping, addressing complex response and background challenges.

Findings

Efficient parallel implementation significantly reduces computation time.

Successful application to 7 years of COMPTEL data.

Enhanced image and background estimation accuracy.

Abstract

Large-scale skymapping with COMPTEL using the full survey database presents challenging problems on account of the complex response and time-variable background. A new approach which attempts to address some of these problems is described, in which the information about each observation is preserved throughout the analysis. In this method, a maximum-entropy algorithm is used to determine image and background simultaneously. Because of the extreme computing requirements, the method has been implemented on a parallel computer, which brings large gains since the response computation is fully parallelizable. The zero level is left undetermined in this method. Results using data from 7 years of COMPTEL data are presented.