Speakers

Dr Anne-Marie Broomhall MInstP

The Music of Stars

The Sun is like a giant musical instrument: In the outer layers of the solar interior, turbulent convection generates sound waves that are trapped inside the Sun. These sound waves have particular frequencies, just like notes on a piano. By observing the notes that the Sun plays, we can work out various things about the solar interior, such as how the temperature varies as we move from the surface to the core. The Sun is a fairly typical star, and so we can apply similar techniques, using observations of oscillations to work out the properties of stars, such as their masses, radii and ages. In this talk though, we will focus on another interesting feature common to both the Sun and other stars: magnetic fields. These fields are generated and maintained by a dynamo inside the stars, but our understanding of how and where is limited. We will discuss the impact internal magnetic fields have on the music of the stars and what that can tell us about solar and stellar magnetism.

Dr Wing Ying Chow MInstP

Biological Solid State NMR

Abstract to follow

Professor Rachel Edwards MInstP

Seeing Sound

Imagine being able to spot flaws in a rollercoaster, railway line, or power station before disaster strikes - all without laying a finger on them. Welcome to the world of non-destructive testing, where engineers use sound to uncover hidden dangers like cracks and corrosion. The standard method of ultrasound testing involves the same gel couplant used in medical ultrasound, but researchers are seeking out more optimal alternatives. But what if we could ditch the gel and make sounds using electricity or lasers instead? Or better yet - what if we could see sound ripple through a structure, interacting with defects in real time? This talk looks at new ways of generating and detecting sound, and the possibility of new sensors to "see sound".

John Marshall

Artificial Neural Networks for Neutrino Physics

Neutrinos are some of the most abundant particles in the universe, but they couple only to the weak interaction (and gravity) and interact very rarely. This makes studying neutrinos very challenging. Physicists typically need to: i) manufacture intense beams of neutrinos (e.g. by focusing charged pions, which then decay to produce neutrinos) and ii) deploy enormous detectors in the beam to record any neutrino interactions that occur.

Detector technologies are evolving rapidly, and some of the latest neutrino experiments are using Liquid-Argon Time-Projection Chamber (LArTPC) detectors, capable of recording “photograph-quality” images of the paths of charged particles emerging from neutrino interactions. The recorded images of interactions can be so detailed, and so complex, that the next big challenge is to accurately interpret images and to “reconstruct” what happened in the interactions. Modern artificial intelligence and machine learning approaches are well-suited to this task. This talk will introduce some of the key points about neutrinos and LArTPC detectors, and then demonstrate how artificial neural networks (ANNs) can be deployed to help interpret and classify neutrino interactions.

Professor Tom Montenegro-Johnson

XOXO: Gossip Gel

This talk will introduce the weird and wonderful mathematics of hydrogels, and discuss what happens when hydrogels are tuned to respond to stimuli in their environment, such as ambient temperature or oscillating chemical reactions.