Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Abstract

Compressive sensing (CS) is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient astrophysical events. This architecture reconstructs a differenced image, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of importance as the information required for generating time- series photometric light curves is best obtained from an image differenced with a reference image. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) reconstruct a dense field, and then apply differencing on the image to obtain the time-ordered photometry. We study the case of gravitational microlensing in which a distant source star in a crowded field is briefly magnified by the passage of a mass through the line of sight between the source star and observer. Our results show that this architecture is able to reconstruct the light curve for magnification factors greater than 1 with error less than 2% using only 10% of the Nyquist rate samples.