Benjamin Pope

Studying Stellar Flares and Cosmic Explosions - using Tree Rings!

Honours Projects Available

Radiocarbon dating is used by archaeologists to determine the ages of wooden artefacts and remains of living things, by measuring how much carbon-13 has decayed to carbon-12 since the material last took in fresh carbon from the air. The amount of carbon-13 in the atmosphere has slowly varied over time due to solar activity and volcanic eruptions, so to calibrate their radiocarbon dates, archaeologists use precise measurements of tree rings of known age. With alternating patterns of slow and fast growth, tree rings form a barcode pattern that can be matched to libraries stretching back millennia, giving us precise radiocarbon references for almost any year since the last Ice Age.

In 2012, a remarkable discovery was made by Fusa Miyake: in 774 AD, there was a huge spike in radiocarbon all over the world, that decayed over the course of a year or two, and may have been associated with powerful aurorae noted by mediaeval monks. It was almost certainly astrophysical in origin. Now several 'Miyake events' have been discovered, and astronomers wonder: was this a powerful solar flare? A supernova? Or the result of a 'magnetar burst', the powerful blast of a magnetised neutron star rearranging itself.

New radiocarbon data in the IntCal20 record are ripe for analysis, by statistically digging into the vast new dataset to find new Miyake events hiding in the noise. We can then determine the true rate of their occurrence, their amplitude and timing, and help narrow down the astrophysical origin of this event. This is an ideal Honours project for a student with strong Python skills and an interest in statistics and interdisciplinary studies.

The Dawn of Exoplanet Radio Astronomy

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Honours Projects Available

Thousands of exoplanets have been discovered with optical astronomy, whether by using the Doppler effect to see the planet pulling its star back and forth (the radial velocity method, which won Didier Queloz & Michel Mayor the 2019 Nobel Prize in Physics), or looking for the dip in brightness as a planet passes in front of a star (the transit method). In our Solar System, the Sun, Earth, and the gas giants are bright sources of radio waves, which tell us a great deal about their magnetic fields and interactions, and for decades astronomers have searched for these effects in more distant planetary systems. Only a couple of the closest stars have been detected in radio waves - otherwise most radio sources are exotic stellar remnants, or black holes at the centres of galaxies. A handful of planets have been discovered by radio astronomy around neutron stars, but the search for radio emission from exoplanets around ordinary stars has until recently not yielded results.

A controversial new discovery by the LOFAR radio telescope in Europe may have opened the window to an important new way to discover and understand exoplanets. LOFAR has found the nearby old, quiet red dwarf GJ 1151 was found to emit circularly polarized radio waves, which we have interpreted as evidence of star-planet magnetic interaction. RV instruments have searched for such a short-period planet, with inconclusive and controversial results. There are other systems we don't believe to be from planetary interactions, but might be new and interesting stellar astrophysics. With many more detections on the way from LOFAR, this could be the beginning of exoplanet radio astronomy. LOFAR is a pathfinder for the recently-approved Square Kilometre Array to be built in Australia, which will be nearly an order of magnitude more sensitive, with the potential to discover hundreds or thousands of these systems.

I have been involved in this project from the beginning, particularly in optical follow-up to search for these planets and understand these stars with ground-based telescopes and satellite data. I have also published theoretical work on what to expect from exoplanet radio observations. There are Honours and PhD projects available in:

• Helping radial-velocity and TESS photometry follow-up of radio-detected stars
• Theoretical predictions of discoveries with the SKA

These would be great projects for students wanting to do a lot of classical observational astronomy, or who want to get to grips with plasma physics.

Designing Particle Accelerators with Automatic Differentiation

An Honours project offered in collaboration with Dr Tessa Charles, University of Liverpool, UK.

In a recent paper, our group showed you could use the technology underlying deep learning - automatic differentiation - to design complicated optical systems. Using software like TensorFlow or Google Jax, you can calculate exact derivatives of the outputs of almost any numerical code - such as a physics simulation. This means if you can simulate a system, you can design improvements by gradient descent - or roll it together with a neural network for 'simulation intelligence' machine learning!

With my collaborator Tessa Charles, we think we can apply these ideas beyond just optics, and improve particle accelerator technology.

The simplest particle accelerator component to model and optimize, we think, would be a "bunch compressor" - a set of just 4 dipole magnets that bends a bunch of particles in a beam out and back again, compressing it longitudinally. We would have to simulate a Monte Carlo particle transport model accounting for the "microbunching instability", in which electron bunches self-interact via synchrotron radiation. Then we should be able to optimize magnet settings to achieve tighter particle bunches - and a proof of concept for how to design the next generation of particle accelerators.

Optical Design and Data Analysis with Automatic Differentiation

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Honours Projects Available

Looking for a planet next to a star is like looking for a firefly next to a searchlight - a planet might be millions of times fainter if you're lucky, and the Earth would be billions of times fainter than the Sun to distant astronomers. The problem gets worse: if you shine a laser at a wall, you'll notice you get a big cloud of speckles around the central dot. The same thing happens looking at a star with a telescope: any optical distortions introduce clouds of speckles that spread starlight out over a wide area. Our pale blue dot would be completely washed out with stray light from the Sun, and a key challenge in astronomy is figuring out ways to manage and suppress starlight to see faint objects nearby.

Many optical instruments exist for doing this - for example coronagraphs and interferometers - but they are a challenge to design and implement. Alternatively, you can use plain old telescopes and try to correct the speckles in post-processing on a computer. This is going to be hugely important for the new ten-billion-dollar James Webb Space Telescope to fulfil its promises.

The key technology that underlies machine learning is automatic differentiation - you want to train a neural network with a million parameters, so you want to calculate exact derivatives of the loss function and use gradient descent. Thanks to the massive investment in machine learning from tech giants like Google, there are now software packages like TensorFlow and Jax that let you differentiate essentially any numerical functions you can write in (say) Python.

We can now use this to optimize optical systems by gradient descent - the shapes and sizes of mirrors, the patterns on phase plates, and many other things. We can also use the same software to help generate better post-processing corrections.

This is an opportunity for pretty open-ended, computationally-intensive PhD and Honours projects for students with an interest in machine learning, computer science, and high precision astronomy. You might:

• Simulate and optimize coronagraphs
• Measure the warping of the Hubble Space Telescope from archival data
• Help better understand and commission the James Webb Space Telescope optics!