AI in Action: AI's early proving ground: the hunt for new particles

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Science  07 Jul 2017:
Vol. 357, Issue 6346, pp. 20
DOI: 10.1126/science.357.6346.20

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Particle physicists began fiddling with artificial intelligence (AI) in the late 1980s, just as the term "neural network" captured the public's imagination. Their field lends itself to AI and machine-learning algorithms because nearly every experiment centers on finding subtle spatial patterns in the countless, similar readouts of complex particle detectors—just the sort of thing at which AI excels. Particle physicists strive to understand the inner workings of the universe by smashing subatomic particles together with enormous energies to blast out exotic new bits of matter, such as the the long-predicted Higgs boson, which was discovered in 2012 at the world's largest proton collider, the Large Hadron Collider (LHC) in Switzerland. Such exotic particles don't come with labels, however. In a fraction of a nanosecond, they decay into other particles, and physicists must spot all those more-common particles and see whether they fit together in a way that's consistent with them coming from the same parent--a job made far harder by the hordes of extraneous particles in a typical collision. Machine-learning algorithms excel in sifting signal from background and are likely to become more important to the field, as the torrents of data from machine such as the LHC continue to increase.

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