High Energy Beams (2022)

Uni-pin fineliner on Fabriano A3 paper, plotted with an Axidraw V3/A3, from SVGs generated by custom Typescript code. Plotted in July 2022.

This series of drawings was inspired by images captured from bubble chambers — a type of physics apparatus that captures traces of exotic particles to understand the patterns behind their behaviour. Bubble chambers, and the way they illuminated the relationship between different particles, were such an important tool that they won their inventor, Donald Glaser, the Nobel Prize. The algorithm I have written to emulate the photographs taken of bubble chambers starts by creating "beams". The beams enter from the right hand side of the page, and curve according to the "charge" assigned to them. They may also randomly undergo fission, forming two new paths each with a proportion of the original charge. Some are not drawn and travel without effort, but the beams that are visible are slowed down by the process, their curves becoming more pronounced as they spiral to a standstill.

But these marks are not a serious attempt to capture the physics of the original traces. They're instead an attempt to recreate the feeling of wonder I feel looking at them. Mysteries of the universe, dimly revealed, grainy & covered in the annotations of those who are seeking to understand them. I want to draw you into trying to understand these diagrams, reaching for the meaning of each mark, noting the relationship between this element and this other, seeing certain patterns repeating, and ultimately to get a sense that there is more understanding lurking deeper than you are able to penetrate.

r_g_b

Digital images generated with custom convolution code (available in browser at https://v21.io/r_g_b.html). February 2022.

This artwork is an algorithm that explores what I call "iterative convolution" — the process of repeatedly feeding the output of a convolution filter back into that convolution filter. This process is controlled by both myself and the machine: the fine parameters of the algorithm slowly shift by themselves, while I respond, controlling in real time the sharpness and scale of the transformation.

Convolution is one of the building blocks of machine learning, used in almost all AI processes involving images. One could say that much of machine learning is just about trying to find the correct set of convolution parameters to achieve a certain task. Going further back, convolution is the fundamental technique underlying the blurring, sharpening and edge detection filters that will be familiar to anyone using image editing software. This r_g_b project is part of a series of works exploring the aesthetic implications of these image kernels. What forms do these processes create; what does it look like to wander randomly through this state space?

The name ‘r_g_b’ is derived from the seed used for these images: just three dots, one of each of red, green and blue. Each colour channel is processed in parallel, the forms varying independently, but also evolving according to the same parameters. This builds on previous work I have made with a single greyscale channel being manipulated, and also extends the system to function across larger scale forms, bringing out, in places, an almost analogue graininess from a purely digital system.