It is the next “big hunt” in modern physics. Following confirmation of the existence of the Higgs Boson earlier this year, physicists now have their eyes on dark matter. With more than four times as much of the elusive stuff believed to be out there than visible matter, finding it would be of arguably even greater significance than the discovery that gained Peter Higgs and François Englert the Nobel prize last month.
Now more people than ever are joining the search party, even non-scientists in their spare time. It’s probably not worth turning out your cupboards to see if there’s any dark matter lurking at the back, but there is a way that you can contribute – at least, if you have the right skills.
Physicists believe dark matter exists because without its gravitational pull, galaxies would fly apart. Yet we can't actually see the stuff because it doesn’t appear to absorb or emit light. There are several theories for what dark matter might be, but they all have to start from negative clues: what we don’t know, or what it doesn’t do.
The current favourite invokes a new fundamental particle called a weakly interacting massive particle (Wimp). “Weakly interacting” means Wimps can, in theory, pass straight through ordinary matter, but occasional collisions with it generate little bursts of light with no other discernible cause. Such flashes would be the telltale signs of dark matter.
Several dark-matter detectors looking for these rare dark-matter flashes are underground, at the bottom of deep mineshafts. One such experiment called LUX looks for collisions of Wimps in a tank of liquid xenon 1.6km underground in a mine in South Dakota. The scientists running LUX recently reported that none had been identified during the first few months it has been in operation. This might not be such a big deal if it wasn’t for the fact that some earlier experiments have reported a few unexplained events that could possibly have been caused by Wimps. As LUX is one of the most sensitive dark-matter experiments currently operating, the new results suggest that the earlier, enticing findings were a false alarm.
At the same time, on board the International Space Station, the Alpha Magnetic Spectrometer (AMS) experiment is looking for signals from the mutual annihilation of colliding Wimps. And there are hopes that the Large Hadron Collider at Cern in Geneva might, once it resumes operations next year, be able to conduct particle smashes at the energies which might produce Wimps from scratch.
In the meantime, the more information we can collect about dark matter in the cosmos, the better placed we are to figure out where and how to look for it. That’s the motivation for making more detailed astronomical observations of galaxies where dark matter is thought to reside. Doing so could help to deduce basic properties of the mysterious particles, such as whether they are "cold" and easy slowed down by gravity, or hot and thus less easily retarded.
One way of doing this is to look for dark matter via its “gravitational lensing” effect. As Einstein’s theory of general relativity predicted, gravitational fields can bend light. This means that dark matter can act like a lens: the light coming from distant objects can be distorted when it passes by a dense clump of matter. David Harvey of the University of Edinburgh, Thomas Kitching of University College London, and their coworkers are using this lensing effect to find out how dark matter is distributed in galaxy clusters, where dark matter can outweigh ordinary matter by a factor of up to 100.
To do that, they needed a computational method to convert observations of gravitational lensing caused by galaxy clusters into its inferred dark-matter distribution in an accurate and efficient manner. Methods already exist, but the researchers suspected they could do better. Or rather, someone else could – so they turned to the wisdom of the crowd.
Crowdsourcing as a way of gathering and analysing large bodies of data is already well established in astronomy, most notably in the Zooniverse project, which encourages volunteers to classify data into different categories: sorting galaxies into their shapes to help improve understanding of how they formed, for example. Humans are often better than machines at making these judgements.
However Harvey and colleagues needed more expertise from the crowd to create their algorithm. So they turned to Kaggle, a web-based service that connects people with a large data-set to data analysts who might be able to crunch it for them. Last year Kitching and his international collaborators used Kaggle to generate the basic gravitational lensing data for dark-matter mapping. Earlier this month they reported that even the analysis of the data can be effectively outsourced this way.
The researchers presented the challenge in the form of a competition called Observing Dark Worlds, in which the authors of the three best algorithms would receive cash prizes of $20,000 donated by the financial company Winton Capital Management. They found the prizewinners could pinpoint dark matter clumps around 30% more accurately than a standard, existing algorithm designed to do the same thing.
The overall winner was Tim Salismans, who this year gained a PhD in analysis of big data. The other two winners were professionals too.
Such recruitment of people with expert skills arguably marks an evolution in the idea of what can be achieved with crowdsourcing. It is not just about soliciting routine, low-level effort from an untrained army of volunteers. Now it is about widening the pool of skilled individuals applying their talents to a problem.
Given how elusive dark matter has proved so far, the search needs all the help it can get.