The pixel detector at CERN’s CMS experiment records particles that emerge from collisions.Credit: CERN

Physicists at the world’s leading atom smasher are calling for help. In the next decade, they plan to produce up to 20 times more particle collisions in the Large Hadron Collider (LHC) than they do now, but current detector systems aren’t fit for the coming deluge. So this week, a group of LHC physicists has teamed up with computer scientists to launch a competition to spur the development of artificial-intelligence techniques that can quickly sort through the debris of these collisions. Researchers hope these will help the experiment’s ultimate goal of revealing fundamental insights into the laws of nature.

At the LHC at CERN, Europe’s particle-physics laboratory near Geneva, two bunches of protons collide head-on inside each of the machine’s detectors 40 million times a second. Every proton collision can produce thousands of new particles, which radiate from a collision point at the centre of each cathedral-sized detector. Millions of silicon sensors are arranged in onion-like layers and light up each time a particle crosses them, producing one pixel of information every time. Collisions are recorded only when they produce potentially interesting by-products. When they are, the detector takes a snapshot that might include hundreds of thousands of pixels from the piled-up debris of up to 20 different pairs of protons. (Because particles move at or close to the speed of light, a detector cannot record a full movie of their motion.)

From this mess, the LHC’s computers reconstruct tens of thousands of tracks in real time, before moving on to the next snapshot. “The name of the game is connecting the dots,” says Jean-Roch Vlimant, a physicist at the California Institute of Technology in Pasadena who is a member of the collaboration that operates the CMS detector at the LHC.

The yellow lines depict reconstructed particle trajectories from collisions recorded by CERN’s CMS detector.Credit: CERN

After future planned upgrades, each snapshot is expected to include particle debris from 200 proton collisions. Physicists currently use pattern-recognition algorithms to reconstruct the particles’ tracks. Although these techniques would be able to work out the paths even after the upgrades, “the problem is, they are too slow”, says Cécile Germain, a computer scientist at the University of Paris South in Orsay. Without major investment in new detector technologies, LHC physicists estimate that the collision rates will exceed the current capabilities by at least a factor of 10.

Researchers suspect that machine-learning algorithms could reconstruct the tracks much more quickly. To help find the best solution, Vlimant and other LHC physicists teamed up with computer scientists including Germain to launch the TrackML challenge. For the next three months, data scientists will be able to download 400 gigabytes of simulated particle-collision data — the pixels produced by an idealized detector — and train their algorithms to reconstruct the tracks.

Participants will be evaluated on the accuracy with which they do this. The top three performers of this phase hosted by Google-owned company Kaggle, will receive cash prizes of US$12,000, $8,000 and $5,000. A second competition will then evaluate algorithms on the basis of speed as well as accuracy, Vlimant says.

Prize appeal

Such competitions have a long tradition in data science, and many young researchers take part to build up their CVs. “Getting well ranked in challenges is extremely important,” says Germain. Perhaps the most famous of these contests was the 2009 Netflix Prize. The entertainment company offered US$1 million to whoever worked out the best way to predict what films its users would like to watch, going on their previous ratings. TrackML isn’t the first challenge in particle physics, either: in 2014, teams competed to ‘discover’ the Higgs boson in a set of simulated data (the LHC discovered the Higgs, long predicted by theory, in 2012). Other science-themed challenges have involved data on anything from plankton to galaxies.

From the computer-science point of view, the Higgs challenge was an ordinary classification problem, says Tim Salimans, one of the top performers in that race (after the challenge, Salimans went on to get a job at the non-profit effort OpenAI in San Francisco, California). But the fact that it was about LHC physics added to its lustre, he says. That may help to explain the challenge’s popularity: nearly 1,800 teams took part, and many researchers credit the contest for having dramatically increased the interaction between the physics and computer-science communities.

TrackML is “incomparably more difficult”, says Germain. In the Higgs case, the reconstructed tracks were part of the input, and contestants had to do another layer of analysis to ‘find’ the particle. In the new problem, she says, you have to construct thousands of helix arcs — the shape of the decay products’ tracks — from roughly 100,000 data points. She thinks the winning technique might end up resembling those used by the program AlphaGo, which made history in 2016 when it beat a human champion at the complex game of Go. In particular, they might use reinforcement learning, in which an algorithm learns by trial and error on the basis of ‘rewards’ that it receives after each attempt.

Vlimant and other physicists are also beginning to consider more untested technologies, such as neuromorphic computing and quantum computing. “It’s not clear where we’re going,” says Vlimant, “but it looks like we have a good path.”