Classifying the Universe with Bayesian Neural Networks
By: Joe Kennedy
Tags: Cosmology, Dark Matter, Machine Learning, Artificial Intelligence, Editors Pick

The Invisible Universe

It is perhaps a rather uncomfortable fact that, despite decades of eyes glued to telescopes and scratches imparted on astronomer’s foreheads, we still do not know what most of the Universe is made of.



Fortunately, artificial intelligence may finally help resolve this frustratingly persistent question. Here’s how.

Everything that we can see with our telescopes, including every planet, star and galaxy, comprises but a tiny percentage of the total constituents of the Universe. There is definitely more stuff out there. Astronomers know this because where there is matter there is a gravitational pull, even if the matter is invisible. This pull influences the movement of galaxies and even light. From observations such as these, we now know that approximately thirty percent of the Universe is filled with this dark matter.

If this wasn’t strange enough, the attempt to understand what remains has caused many a headache in the cosmology community. Labelled dark energy, this mysterious substance is thought to drive our Universe’s current phase of accelerated expansion. Our best guess is that dark energy arises from a constant energy inherently associated with empty space, although getting this idea to fit into a cohesive theoretical framework has proved doggedly difficult. Another possibility is that we do not yet understand how Einstein’s theory of General Relativity works on the vast cosmological distances where dark energy begins to dominate. In the same way that the movement of electrons in an atom isn’t affected by gravity, an additional “fifth force’’ could kick into gear when we consider the size of the observable Universe, potentially driving cosmic acceleration.

Theoretical physicists require a well-functioning imagination to wrap their heads around these ideas, and these imaginations have produced a plethora of possibilities which attempt to explain what dark matter and dark energy, collectively known as “the dark sector’’, really are [1]. But how does one go about separating the theoretical wheat from the theoretical chaff?

One key method is to look at how strongly dark matter clusters together at different length scales in the Universe, quantified by something called the dark matter power spectrum. Different theories give characteristic predictions for the shape of this power spectrum. Imagine a theory which slightly strengthens gravity and thus enhances the pull a clump of dark matter exerts on the surrounding dark matter. If this were the case, one would hope to see this in the dark matter power spectrum.

The astute among you may have gathered that, by virtue of being dark, astronomers cannot directly measure the shape of the dark matter power spectrum. Rather, it must be inferred from the influence dark matter has on say, galaxies or light trajectories. Generally speaking, the more galaxies tend to group together or light seems to be bent, the more dark matter there is at that particular spot.

Over the next decade, many new cosmological surveys will map out the positions of literally billions of galaxies, providing us with the most detailed map of the cosmos ever produced. Cosmologists hope to use this galaxy map to create a map of the Universe’s dark matter and thus provide us with our Universe’s very own dark matter power spectrum. The question is, once we obtain this data, how can it be translated into a deeper understanding of the fundamental nature of the dark sector?