Particle theory

Abstract:

We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models. Both supervised and unsupervised learning are considered. We find that, for a fixed compactification manifold, relatively small neural networks are capable of distinguishing consistent line bundle models with the correct gauge group and the correct chiral asymmetry from random models without these properties. The same distinction can also be achieved in the context of unsupervised learning, using an autoencoder. Learning nontopological properties, specifically the number of Higgs multiplets, turns out to be more difficult, but is possible using sizeable networks and feature-enhanced datasets.

Abstract:

We present an all-sky 90% confidence level upper limit on the cosmic flux of relativistic magnetic monopoles using 2886 days of IceCube data. The analysis was optimized for monopole speeds between 0.750 c and 0.995 c , without any explicit restriction on the monopole mass. We constrain the flux of relativistic cosmic magnetic monopoles to a level below 2.0 × 10 − 19     cm − 2   s − 1   sr − 1 over the majority of the targeted speed range. This result constitutes the most strict upper limit to date for magnetic monopoles with β ≳ 0.8 and up to β ∼ 0.995 and fills the gap between existing limits on the cosmic flux of nonrelativistic and ultrarelativistic magnetic monopoles.