Using Hilbert curves to organize, sample, and sonify solar data

Abstract

How many ways can we explore the Sun? We have images in many wavelengths and squiggly lines of many parameters that we can use to characterize the Sun. We know that while the Sun is blindingly bright to the naked eye, it also has regions that are dark in some wavelengths of light. All of those classifications are based on vision. Hearing is another sense that can be used to explore solar data. Some data, such as the sunspot number or the extreme ultraviolet spectral irradiance, can be readily sonified by converting the data values to musical pitches. Images are more difficult. Using a raster scan algorithm to convert a full-disk image of the Sun to a stream of pixel values creates variations that are dominated by the pattern of moving on and off the limb of the Sun. A sonification of such a raster scan will contain discontinuities at the limbs that mask the information contained in the image. As an alternative, Hilbert curves are continuous space-filling curves that map a linear variable onto the two-dimensional coordinates of an image. We have investigated using Hilbert curves as a way to sample and analyze solar images. Reading the image along a Hilbert curve keeps most neighborhoods close together as the resolution (i.e., the order of the Hilbert curve) increases. It also removes most of the detector size periodicities and may reveal larger-scale features. We present several examples of sonified solar data, including sunspot number, extreme ultraviolet (EUV) spectral irradiances, an EUV image, and a sequence of EUV images during a filament eruption.