If aliens exist, they’ll find political union as tough as we do


I’ve thought a great deal about the so called Zoo solution to Fermi’s Paradox over the last five years. This is the idea that intelligent aliens have decided not to reveal themselves to us, and this is why we see no signs of intelligent life.

It’s quite an unscientific solution, as we cannot gather evidence in favour of it.  If we could, then we would have proof of intelligent life, and that would be the end of it!

The question now is: can we prove (or disprove) this solution while still having no evidence for aliens? The best we can do at this stage is challenge the assumptions that we must make to support it.

This is what I did in my latest paper.  The critical assumption about solutions that forbid contact with us (the Zoo Solution, the Prime Directive, the Interdict Hypothesis) is that this requires some form of law or moral tradition. Laws require agreement between multiple parties – in this case, most likely multiple species of intelligent life, from a variety of different backgrounds with very different belief systems.  To use Carl Sagan’s line, these solutions need a “Galactic Club” – underwritten by a host of treaties – to work.

So how can we test if a “uniformity of motive” can be established? I ran some computer simulations that were in effect an experiment in uniformity. In each experiment, I assumed that there N civilisations would appear in the Galaxy over its lifetime.   I placed them at given locations at a given time. I then tested whether a signal from civilisation a) would reach civilisation b) before b) became advanced enough to receive it. In other words, I wanted to measure how much civilisation a) can influence civilisation b).

This is a strict condition for universal interstellar law to emerge. It’s true that we can relax this condition by allowing civilisations to evolve after they make contact, but I wanted to see whether a universal legal system is a natural consequence in a populated Galaxy.

And, surprise surprise, it isn’t. In previous work, I was able to show that Galactic Clubs were very unlikely. In my latest research, I was able to quantify what the Club would be replaced with. More often than not, my simulations showed a large number of small civilisation groupings, which I call “Galactic cliques”.  It’s quite likely that these cliques do not share much in common culturally (at least initially).

So we can conclude that Galactic Clubs are far from guaranteed. In fact, to obtain a single Club the distribution of civilisations in space and time must be quite odd (you can read the paper to find out more), or one clique must dominate the others well after the fact (via political or military means).

This work hardly destroys these solutions to Fermi’s Paradox. What it does show us is the weaknesses in the assumptions we make to propose these solutions.  The Galaxy is big enough that it would be pretty tough for every civilisation to come to an agreement about how to conduct themselves.

This is a consequence of the laws of physics, and it holds true whether there are ten or ten thousand civilisations out there.

It’s certainly true even if there’s only one, as our own civilisation can attest to.

How Do We Find Interesting Things in Very Large Simulations?

It’s a growing problem in computational astrophysics.  Hydrodynamic simulations (say of giant molecular clouds and star forming regions) are getting very large.  When we want to analyse them and find interesting features to compare to the physical Universe, simply searching them “by eye” is becoming an enormous task.

One simple solution to this is to farm out the problem to citizen scientists, essentially doing the “by eye” hundreds of thousands of times in a few days.  This technique is great if you can break up the simulation into easily viewable chunks for each citizen scientist to look at.  But what if you can’t do this, or you don’t have access to millions of enthusiastic people?

We must rely on algorithms to solve this problem.  Luckily cosmologists came across similar issues in N-Body simulations of dark matter quite some time ago (click here for some images and movies of simulations done around 15 years ago).  These simulations have slightly less physics inside, and hence grew to large data sizes much quicker, which was essential to modelling the growth of structure on cosmic scales.  They used something called tensor classification to analyse the mass distribution.

This is a technique which relies on computing a rank 2 tensor, a matrix, which contains information about how the simulation changes with position over all 3 dimensions.

For example, we can compute a tidal tensor, which is two derivatives of the gravitational potential.  This measures how the gravitational force changes as a function of position.  Manipulation of the tensor (finding its eigenvalues and eigenvectors) allows us to say what shapes and geometries the gravitational force is trying to build.  Is it making pancake-like sheets? Rope-like filaments? Or is it squeezing everything into a sphere? Or is it doing none of this, and is instead creating a void?

This technique gives cosmologists useful information about the filamentary structure of dark matter on very large scales.  In a recent paper, I investigated how these N-Body methods (where the only force active is gravity) could be ported into hydrodynamic calculations (where pressure forces, radiation and perhaps magnetic fields also play a role).

As we work with smoothed particle hydrodynamics (SPH), which also simulates a fluid using particles, these methods are easy to apply, with the advantage that there are less free parameters in the calculation.

And it has some stunning uses.  Want to find the spiral arms in a self-gravitating disc? Presto:



Want to trace the blast wave of a supernova as it travels through interstellar gas? Sure:



It is also quite good at detecting filaments in molecular clouds, but the results aren’t quite as impressive – yet.  I have a student working on this problem as we speak, and I’m hoping for exciting results.

We’ve really only just begun using tensor classification for problems like this, and there are some great possibilities for analysing other fields such as the magnetic field and radiation fields.  We might even be able to generalise this to fully relativistic calculations and compute structures in distorted space-time.

Hopefully you’ll be reading future posts on how I’ve put this technique to great use!

Squishier moons are better for life

My more eager readers will have noticed a sudden flurry of submissions to the arXiv since Christmas. I’ll try and bring you up to date with what I’ve been publishing recently, which is hitting a variety of topics.

The first is a return to a favourite area of research for me: exomoon habitability. As you can see from earlier posts, I’ve been looking at this subject for a while now, focusing in particular how an Earthlike object would fare orbiting a giant planet, which in turn orbits a star like our Sun.

I’ve been using a simple, 1D climate model to follow the temperature changes on this Earth copy, and discover what sort of orbital parameters might be needed for it to possess surface liquid water. But we’ve known from the start that these climate models have been overly simple, and in some cases they’ve missed out important physics that might affect our answers.

In our latest work, we took aim at two aspects of the model that we felt were lacking. In the first, we investigated the issue of atmospheric composition. Up until now, we had assumed a fixed composition, but we know that the Earth has adjusted its atmosphere over time. Sometimes, this is due to the presence of life (like the great oxygenation events that are responsible for the good stuff filling your lungs), but other processes play a role.

In fact, we can identify a complete cycle of processes that affect the total amount of carbon dioxide. CO2 is emitted into the atmosphere via volcanic eruptions. It then precipitates into rain, which falls into the sea, and gets incorporated into rocks and seashells on the ocean floor. This ocean floor is eventually sub ducted at a tectonic plate boundary, and returned to the mantle, where the whole cycle begins again.


It’s known as the carbonate-silicate cycle (because the rocks in play are carbonate and silicate rocks). What’s rather clever about this system is that it acts as a thermostat. If the planet starts to warm, then more Co2 rains out of the atmosphere and into the oceans than is expelled by volcanoes, which reduces the amount of Co2 in the atmosphere. As Co2 is a greenhouse gas, getting rid of it allows the planet to cool more easily. If the planet cools, less co2 rains out and is added to by volcanism. A little extra greenhouse gas helps the planet keep its heat. This is why scientists are worried about manmade co2 production, as we’re mucking around with the thermostat, and too much fiddling could break it.

We never considered this process before, so we changed our model to allow the co2 levels of our moon to vary to help keep the temperature warm and stable.

We then turned to the tidal heating of our moon, which until this point was done using a rather simple model. As you may already know, tidal heating is generated by the planet’s gravity stretching and squeezing the moon as it goes around its orbit. Crucially, the amount of heat the planet can generate in the moon depends on the material the moon is made of, as well as what state it’s in. If the heating is intense enough to allow melting, this can reduce the tidal heating, and stop moons from becoming too hot for life.

We found that when we added both effects, the habitable zone for the moon moves further away from the star.  It also gets wider around the planet as well – moons can orbit closer without being roasted, and changing CO2 levels lets the moon stay warm further away from heat sources by boosting its greenhouse effect.

We’re far from the final answer here: one day I hope to be telling you about fully 3D models of exomoon atmospheres.  Even 1D models like ours still need some extra physics, like investigating how changing the spectrum of incoming radiation affects this answer.  But every step is a step forward!