Communications of the ACM
Physicists Develop New Techniques to Enhance Data Analysis for Large Hadron Collider
Researchers at New York University developed new machine learning techniques to improve data analysis for the Large Hadron Collider, the world's most powerful particle accelerator.
Credit: CERN
New York University (NYU) researchers have developed machine learning techniques that can significantly improve data analysis for the Large Hadron Collider (LHC), the world's most powerful particle accelerator.
The researchers had previously developed statistical tools and methodology to perform measurements of the Higgs boson.
The new methods offer the possibility of additional breakthrough discoveries.
NYU researcher Kyle Cranmer says simulations often provide the best descriptions of a complicated phenomenon, but they are difficult to use in the context of data analysis. For example, he says it is easy to simulate the break in a game of billiards, but it is much more difficult to look at the final position of the balls to infer how hard and at what angle the cue ball was initially struck.
Said Cranmer, "The techniques we've developed build a bridge allowing us to exploit these very accurate simulations in the context of data analysis."
From New York University
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