Image Subtraction in Fourier Space 后印本

摘要: Image subtraction is essential for transient detection in time-domain astronomy. The point spread function (PSF), photometric scaling, and sky background generally vary with time and across the field-of-view for imaging data taken with ground-based optical telescopes. Image subtraction algorithms need to match these variations for the detection of flux variability. An algorithm that can be fully parallelized is highly desirable for future time-domain surveys. Here we show the Saccadic Fast Fourier Transform (SFFT) algorithm for image differencing. SFFT uses $\delta$-function basis for kernel decomposition, and the image subtraction is performed in Fourier Space. This brings about a remarkable improvement of computational performance of about an order of magnitude compared to other published image subtraction codes. SFFT can accommodate the spatial variations in wide-field imaging data, including PSF, photometric scaling, and sky background. However, the flexibility of the $\delta$-function basis may also make it more prone to overfitting. The algorithm has been tested extensively in real astronomical data taken by a variety of telescopes. Moreover, the SFFT code allows for the spatial variations of the PSF and sky background to be fitted by spline functions.