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. 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.