Potential applications of machine learning in particle physics
presentationposted on 29.03.2018, 02:34 by Andrew Fowlie
We, the GAMBIT collaboration, perform statistical analyses of models in particle physics. We must determine whether points in a high-dimensional parameter space are forbidden or allowed by a variety of experiments, including searches at the Large Hadron Collider. This is a computationally expensive calculation and could benefit from classification algorithms in ML. Finally, we must visualise and understand our high-dimensional parameter space; this could benefit from clustering and dimensional reduction.