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

W Dean Pesnell,Kevin Addison,Kyle Ingram-Johnson

doi:10.1119/10.0005403

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.

Highlights

Sonifying a dataset has the basic purposes of making data accessible to the blind and allowing the data to serve as an adjunct to other senses
The data have variations in two dimensions that should be represented by the sonification, and variations seen in a series of images are even more difficult to sonify
We reduced some of the noisy variations at low pixel values by replacing the dark regions with a rest, but the sonification still does not reveal much about the image other than the broad shape of the Sun

Summary

Introduction

Sonifying a dataset has the basic purposes of making data accessible to the blind and allowing the data to serve as an adjunct to other senses. It can help all to appreciate or understand a dataset in a new way. A onedimensional dataset can be sonified by scaling the data to pitches, image data are a more ambitious target. The data have variations in two dimensions that should be represented by the sonification, and variations seen in a series of images are even more difficult to sonify. Solar data are often in the form of images, and the changes in time and space are an integral part of understanding solar variations. We will describe sonifying several solar datasets, including an exploration of ways to sonify solar images in space and time

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Discussion

Conclusion