Application of a Differential-CLEAN Algorithm in the Coded-Mask Imaging

The Coded-Mask imaging method has application in astronomical imaging in hard X-ray and higher-energy bands. The cross-correlation algorithm for image reconstruction may produce ghost peaks. The CLEAN algorithm can effectively remove the ghost peaks, but still cannot avoid the error caused by the irregular data sampling or missing data points. We apply a Differential-CLEAN algorithm to reconstruct coded-mask images. This algorithm operates directly in the detector data space, and evaluates the observed data from models on each detector without data interpolation. It improves image quality by avoiding the deterioration caused by the known problems. Reconstruction results of simulated data, where ideal point-source data with the Poisson noise and artificial bad pixels are used, show that the Differential-CLEAN algorithm has better flux estimations with lower noise levels compared to the CLEAN algorithm. The IBIS, a coded-mask imager on-board the INTEGRAL satellite, has already had a considerable portion of detector units damaged. We also processed certain IBIS data with the Differential-CLEAN algorithm. Compared to the standard scientific data products produced by the INTEGRAL Offline Science Analysis (OSA) software package, which employs the CLEAN algorithm, the results of the Differential-Clean algorithm show lower noise levels.