Trilateral Trieste - Nova Gorica - Ljubljana meeting

The calculation of tunneling actions, that control the exponential suppression of the decay of metastable phases (like the unstable electroweak vacuum), can be reformulated as an elementary variational problem in field space. This alternative approach circumvents the use of bounces in Euclidean space by introducing an auxiliary function, a tunneling potential Vt that connects smoothly the metastable and stable phases of the field potential V. The tunneling action is obtained as the integral in field space of an action density that is a simple function of Vt and V and can be considered as a generalization of the thin-wall action to arbitrary potentials. This formalism provides new handles for the theoretical understanding of different features of vacuum decay, can be easily extended to include gravitational effects in an elegant way and has a number of useful applications that I will discuss.

In this talk I will discuss two different connections between gravitational wave and flavour physics. The first utilises neutron star mergers and pulsar timing measurements to constrain long-range forces coupled to muons, such as gauged Lmu-Ltau symmetries. The second avenue relies on first-order phase transitions arising from sequential stages of symmetry breakings associated to the dynamical generation of the Yukawa matrices.  

The primary goal at the Large Hadron Collider (LHC) is now to discover new physics, often termed Beyond the Standard Model (BSM) physics, and machine learning techniques could prove essential to this discovery. In this talk I will illustrate jet substructure tools, and explain the need for more powerful algorithms to better understand the complex signatures that arise in the LHC data. I will briefly review some of the most important applications of machine learning tools used in studying LHC data, and discuss their advantages and disadvantages. The precise focus of the talk will be on unsupervised searches for BSM physics using Bayesian generative modelling, in particular the Latent Dirichlet Allocation (LDA) algorithm. I will motivate the use of these techniques with an approximate mapping between the process through which particle collisions at the LHC evolve into measurements in the detectors, and the process of document generation described by the LDA model. The goal in using this technique is to extract ‘topics’ from the data, which describe the physics underlaying the signals that have been measured in the collider. With these topics in hand, we can then use them to classify individual signals as having arisen from different underlaying processes. Two advantages of this technique are (i) it is unsupervised and hence insensitive to modelling inaccuracies, (ii) the extraction of topics allows the user to analyse what has been learned by the algorithm. I will conclude the talk with two applications of this technique; the first is in uncovering a pair-produced top quark signal, and the second is in uncovering a W′ signal.