Particle theory

Abstract:

Particle physics is sometimes described as going through a crisis, notably because of the continued lack of discovery of physics beyond the Standard Model, despite the LHC having operated at close to maximal energy for some years now. Here, I argue that we should not underestimate the significance of recent progress and future prospects in the Higgs sector of the Standard Model. This is especially the case for the Yukawa interactions and the structure of the Higgs potential, both of which are unlike any sector that has been established and stress-tested before in particle physics. Other topics that I touch on include the still substantial scope for increasing the reach of searches at LHC, the increasing role of precision in hadron-collider physics and the rich interplay that is developing between heavy-ion and proton-proton physics.

Abstract:

We study F-theory duals of heterotic line bundle models on elliptically fibered Calabi-Yau threefolds. These models necessarily contain NS5-branes which are geometrized in the dual F-theory compactifications. We initiate a systematic study of the correspondence between various configurations of NS5-branes and the dual geometries in F-theory and perform several checks of the duality. Furthermore, we discuss the singular transitions between different configurations of NS5-branes.

Abstract:

Lund diagrams, a theoretical representation of the phase space within jets, have long been used in discussing parton showers and resummations. We point out that they can be created for individual jets through repeated Cambridge/Aachen declustering, providing a powerful visual representation of the radiation within any given jet. Concentrating here on the primary Lund plane, we outline some of its analytical properties, highlight its scope for constraining Monte Carlo simulations and comment on its relation with existing observables such as the zg variable and the iterated soft-drop multiplicity. We then examine its use for boosted electroweak boson tagging at high momenta. It provides good performance when used as an input to machine learning. Much of this performance can be reproduced also within a transparent log-likelihood method, whose underlying assumption is that different regions of the primary Lund plane are largely decorrelated. This suggests a potential for unique insight and experimental validation of the features being used by machine-learning approaches.