Constraining strongly coupled new physics from cosmic rays with machine learning techniques. (6th November 2019)

• Record Type: Journal Article
• Authors: Schichtel, Peter; Spannowsky, Michael; Waite, Philip
• Is Part Of: Europhysics letters. Volume 127:Number 6(2019)
• Publication Date: 2019-11-06
• Subjects: 12.60.-i -- 98.70.Sa; Physics -- Periodicals; Electronic journals; 530.05
• DOI: 10.1209/0295-5075/127/61002
• Languages: English
• ISSNs: 0295-5075

Abstract

Abstract: 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.