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Arthur T Knackerbracket has found the following story:

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.

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.

[...] 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.”

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