Volume 127, Number 6, September 2019
|Number of page(s)||7|
|Section||The Physics of Elementary Particles and Fields|
|Published online||06 November 2019|
Constraining strongly coupled new physics from cosmic rays with machine learning techniques
1 German Research Center for Artificial Intelligence (DFKI) - 67663 Kaiserslautern, Germany
2 Ingenieurgesellschaft Auto und Verkehr (IAV) - 67663 Kaiserslautern, Germany
3 Institute for Particle Physics Phenomenology, Department of Physics, Durham University Durham, DH1 3LE, UK
Received: 1 July 2019
Accepted: 17 September 2019
Cosmic rays interacting with the atmosphere allow for the probing of fundamental interactions at ultra-high energies. We thus obtain limits on strongly coupled new physics models via their imprints on cosmic-ray air showers. Using the Monte Carlo event generators Herwig and HERBVI, and the air shower simulator CORSIKA, to simulate such processes, we apply machine learning algorithms to the simulated observables to discriminate the events arising via new physics from the QCD background. We then use the signal and background discrimination performance to set potential limits on the cross-sections of the new physics models.
PACS: 12.60.-i – Models beyond the standard model / 98.70.Sa – Cosmic rays (including sources, origin, acceleration, and interactions)
© EPLA, 2019
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