Noise-Compensating Algebraic Reconstruction for a Rotating Modulation Gamma-Ray Imager

TL;DR

This paper introduces the Noise-Compensating Algebraic Reconstruction (NCAR) algorithm for rotating modulation gamma-ray imaging, enabling super-resolution and noise suppression, outperforming traditional methods in simulations for astrophysical survey applications.

Contribution

The paper presents NCAR, a novel reconstruction algorithm that enhances image resolution and noise handling in rotating modulation gamma-ray imagers, suitable for high-energy astrophysical surveys.

Findings

NCAR achieves super-resolution in simulated gamma-ray images.

It can resolve multiple sources and complex distributions.

Noise appears as fuzzy sidelobes, reducing false peaks.

Abstract

A Rotating Modulator (RM) is one of a class of techniques for indirect imaging of an object scene by modulation and detection of incident photons. Comparison of the RM to more common imaging techniques, the Rotating Modulation Collimator and the coded aperture, reveals trade-offs in instrument weight and complexity, sensitivity, angular resolution, and image fidelity. In the case of a high-energy (hundreds of keV to MeV), wide field-of-view, satellite or balloon-borne astrophysical survey mission, the RM is shown to be an attractive option when coupled with a reconstruction algorithm that can simultaneously achieve super-resolution and suppress fluctuations arising from statistical noise. We describe the Noise-Compensating Algebraic Reconstruction (NCAR) algorithm, which is shown to perform better than traditional deconvolution techniques for most object scene distributions. Results from Monte Carlo simulations demonstrate that NCAR achieves super-resolution, can resolve multiple point sources and complex distributions, and manifests noise as fuzzy sidelobes about the true source location, rather than spurious peaks elsewhere in the image as seen with other techniques.