Research
- Giant impacts
- Simulations and initial conditions
- Neutron lifetime from space
- Lunar exosphere
- Charge transfer inefficiency
Most of these projects were group collaborations in and outside of Durham – see the papers and other links for the appropriate credits.
— Giant impacts —
Our solar system used to be a much more violent place, with proto-planets colliding in cataclysmic giant impacts that helped create the worlds we see today.
We study these dramatic events with 3D smoothed particle hydrodynamics (SPH) simulations, at 100–1000 times higher resolution than the current standard using our newly developed code SWIFT and a powerful supercomputer. This unprecedented detail allows us to study exciting topics like the tilt and strange internal structure of Uranus, the formation of Saturn’s rings from colliding moons, and atmospheric erosion from giant impacts.
The top banner image is a mid-collision snapshot from a 100 million particle simulation of a grazing giant impact onto the young planet Uranus, and the top animation shows a cross-section of what another of these high-resolution collisions looks like in motion. The colours show the particles’ material (rock, ice, or atmosphere) or their internal energy (similar to their temperature).
– Atmospheric erosion –
The Earth’s atmosphere has a complicated history of being built up and eroded,
and exoplanets around other stars
show a huge variety of atmospheres from very thick to very thin.
Giant impacts might play a key role in this evolution,
but the low density of atmospheres
and the complex messiness of impacts makes it a challenging problem to study,
so previous studies have primarily focused on 1D models or thick atmospheres,
often also limited to only head-on collisions.
We ran 3D simulations of over 300 collisions onto terrestrial planets like hosting thin(ish) atmospheres for the first time spanning a wide range of angles and speeds, as well as different masses and compositions. This lets us examine the mechanisms of how giant impacts erode atmospheres and how much is lost in each case. The animations below show the first few hours of four 108-particle simulations with a mix of head-on, grazing, slow, and fast scenarios.
In spite of the complicated details and the great differences between scenarios, we found that a simple scaling law can be used to estimate the erosion from any collision in this regime, as illustrated by this crowded figure with the results from many dissimilar scenarios overlapping on the same line. This makes it possible to predict the loss from other impacts in the context of large-scale models of planet formation.
– High-resolution simulations and convergence –
Any simulation has limitations. One of the most important tests for a model to be useful is that the answer to your chosen question stays the same when the resolution improves. If not, then regardless of how simple or sophisticated the simulation might be, the results won’t be reliable.
We found that giant impact simulations using the current standard of 105 (top panel) or 106 particles can fail to converge even on bulk outcomes like the rotation period, and get the answer wrong by several hours in this example.
Using SWIFT, we ran simulations with over 107 and 108 (bottom panel) particles and confirmed that these do converge on the large-scale results reliably. Studying finer details like the composition of ejected debris may require even higher resolution.
– Icy moon collisions –
Saturn’s spectacular rings and mid-sized moons were recently discovered to be significantly younger than expected. This led to a new idea that they may have been created out of the debris from collisions in a previous generation of icy moons.
We run high-resolution simulations to study the detail of the ejected debris. What distribution of big and small fragments can survive a collision? How fast and in what directions are any fragments ejected? These results then feed into models of the whole Saturn system to test whether the moons and rings we see today can be made in this way.
This figure shows the post-impact debris
from a simulation of colliding icy moons using ~107.5 particles.
The larger orange-rock and blue-ice target started from the left
and the smaller yellow-rock and purple-ice impactor from the right,
moving left at 3 km s-1.
The insets show zoomed-in regions
to highlight some of the resolved fragments that survive the collision.
– Knocking over an ice giant –
ApJ 2018 Paper • SciFri Radio Segment
Uranus is an odd planet. It spins on its side, with an obliquity of 98° and its major moons orbiting in the same tilted plane. This was most likely caused by a giant impact, which might also help explain other mysteries such as the planet’s extremely cold exterior and strange magnetic field.
We ran the first Uranus impact simulations since the original study in 1992 to study a wide variety of scenarios and the possible consequences of this violent event for the planet. As well as confirming that the impact could knock over Uranus’ spin, we found that with a grazing collision the impactor could form a thin shell around the planet’s ice layer, possibly preventing convection and trapping the interior heat to help explain the freezing outer temperatures.
— Simulations and initial conditions —
The supercomputers we use run on as much electricity as a small town, so it’s important that we use them effectively. As well as running simulations of planets to learn about them, we also work on the code development behind the scenes. This includes big projects like the SWIFT code in collaboration with other astronomers and computer scientists, alongside smaller topics more specific to planetary science.
– SWIFT –
www.swiftsim.com 
SWIFT (SPH With Inter-dependent Fine-grained Tasking)
is a hydrodynamics and gravity code
for astrophysics and cosmology in open development,
designed from the ground up to run fast and scale well
on shared/distributed-memory architectures (supercomputers).
A supercomputer is basically a large number of normal computers working together in “parallel”. For the past decade, instead of getting faster, supercomputers are getting more parallel. This makes it ever more important to share the work evenly between every part of the computer so that no processors are sitting idle and wasting time.
SWIFT’s various careful approaches to these challenges have allowed us to run planetary impact simulations with 1000 times more particles than before, and cosmological simulations of galaxy formation over 30 times faster.
Find out everything about SWIFT and give the code a try with the examples and documentation at www.swiftsim.com.
– Placing particles in spherical shells –
MNRAS 2019 Paper
Python: pip install seagen
Github: github.com/jkeger/seagen
The SPH method we use requires arranging many millions of tiny particles to represent each planet in the computer. The spherical symmetry and sharp boundaries between layers in planets makes it helpful to place particles in nested spherical shells. If some particles are a bit too close or a bit too far away from each other, then our model planet won’t be perfectly stable. In that case, we’d need to run an extra simulation to let it “relax” and for any wobbling to settle down before the impact, using up valuable time on a supercomputer. So we have to arrange these particles carefully.
However, it is impossible to place an arbitrary number of particles equally spaced on a sphere – a long-standing problem in mathematics and other fields, such as chemistry for how atoms arrange themselves in molecules.
We developed a new scheme (and public python module) for placing particles almost perfectly by dividing the sphere into equal, roughly square regions, then stretching particles slightly away from the poles. This “stretched equal-area” (SEA) method ensures that all particles have an SPH density within 1% of the right value.
The figures show an example SEA arrangement of 100 particles on a single shell and of 100,000 particles in nested shells to build up a simple model of an Earth-like planet.
– WoMa: making profiles and spinning planets –
arXiv Paper
Python: pip install woma
Github: github.com/srbonilla/WoMa 
Before we can arrange any particles,
we first need interior models for our planets.
Many objects in astronomy are roughly spherical,
but rotation is also common and can be very important in giant impacts.
We developed a fast method (and open-source python module)
to make models of multi-layered spinning or static bodies
in hydrostatic equilibrium,
and to convert them into particle representations using
a modified version of SEAGen.
Rotating planets have occasionally been studied with particle simulations, but typically the initial conditions had to be made by incrementally spinning up a spherical planet with multiple settling simulations. This can be slow and also makes it impossible to know the precise properties of the final planet until the end of the process. WoMa avoids any extra simulation and also allows us to control everything directly.
We used WoMa to test rotating initial conditions with just over one billion SPH particles, and to study the effects of a spinning impactor Theia on the canonical Moon-forming impact. Just by changing Theia’s initial spin, the collision can result in anything from a complete merger, a hit-and-run, or even a clump surviving in orbit, as illustrated by the 5 scenarios shown in this figure. The lTh labels show the rotational angular momentum of Theia as a fraction of the maximum it could have before becoming unstable.
— Neutron lifetime —
Free neutrons decay in about 880 seconds, but the two types of experiments (“bottle” and “beam”) have a long-standing disagreement on the exact value by a lot compared with their measurement precision. This lifetime is an important parameter for our understanding of the formation of helium in the early universe and key tests of particle physics.
We demonstrated the feasibility of an entirely different way to measure the lifetime from space, using data from the MESSENGER spacecraft as it flew by Venus and Mercury. Neutrons are produced when cosmic rays strike atoms in the surface or atmosphere of a planet. MESSENGER scooped up neutrons at different altitudes above each planet, so by studying how many neutrons survive the long flight to high altitudes we can estimate the lifetime. This was made more complicated by the quirks of MESSENGER’s neutron detectors – which were not intended for this experiment – and the varied composition of Mercury’s surface, amongst other things.
This figure shows the comparison of three models with different neutron lifetimes with the data during the Venus fly-by, for the two detectors that were facing in (a) and opposite (b) the direction of MESSENGER’s flight.
Our measurement is consistent with the lab-based results but, being based on limited data from a mission designed for something entirely different, has a large uncertainty. We are now investigating the best way to measure the lifetime precisely with a new mission.
— Lunar exosphere —
The Moon has an extremely thin atmosphere (a “surface bounded exosphere”) which is so tenuous that the particles can fly around without bouncing off each other. The most abundant component is argon.
Radioactive decay of potassium in the Moon’s outer layers produces argon
that leaks out through the lunar surface,
joining a handful of other gases in the exosphere.
Argon particles hop around for a short while before either being lost due to interactions with radiation from the sun or getting stuck in the extreme “cold traps” in craters around the poles. Studying the lunar argon exosphere can teach us about the solar wind, the lunar interior and outgassing, the efficiency of volatile sticking in polar cold traps and the kinetics of adsorption and desorption in low pressure environments.
By performing Monte Carlo simulations of the transport of argon molecules through the exosphere, we showed that only a localised source of argon can explain the persistent excess observed over the western maria. This figure shows our model as solid lines and the data from the LADEE spacecraft as points. The dashed lines show the alternative hypothesis of locally varying surface interactions.
We also produced the first simulations to show that the long-term fluctuations in the exosphere’s global argon density could be explained by seasonal variations in the polar cold traps.
Our code is available here: icc.dur.ac.uk/~vreke/solarsystem/exosphere/.
— Charge transfer inefficiency —
WIP