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Particle colliders enable us to probe the fundamental nature of matter by observing exotic particles produced by high-energy collisions. Because the experimental measurements from these collisions are necessarily incomplete and imprecise, machine learning algorithms play a major role in the analysis of experimental data. The high-energy physics community typically relies on standardized machine learning software packages for this analysis, and devotes substantial effort towards improving statistical power by hand crafting high-level features derived from the raw collider measurements. In this paper, we train artificial neural networks to detect the decay of the Higgs boson to tau leptons on a dataset of 82 million simulated collision events. We demonstrate that deep neural network architectures are particularly well-suited for this task with the ability to automatically discover high-level features from the data and increase discovery significance.
Author Information
Peter Sadowski (University of Hawai‘i)
Daniel Whiteson (University of California Irvine)
Pierre Baldi (UC Irvine)
Related Events (a corresponding poster, oral, or spotlight)
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2014 Poster: Searching for Higgs Boson Decay Modes with Deep Learning »
Wed. Dec 10th 12:00 -- 04:59 AM Room Level 2, room 210D
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