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Searching for Higgs Boson Decay Modes with Deep Learning
Peter Sadowski · Daniel Whiteson · Pierre Baldi

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None

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)

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