Event Category: 4D Seminar

Abstract: Self-Destructing Dark Matter (SDDM), a new class of dark matter models will be presented In this class of models, a component of dark matter can transition from a long-lived state to a short-lived one by scattering off of a nucleus or an electron in the Earth. The short-lived state then decays to Standard Model particles, generating a dark matter … Read More

abstract: The central region of Supernovae are one of the hottest and densest regions in the Universe. Due to the high temperatures, particles with sub-GeV masses can be copiously produced if they have non-negligible couplings to the Standard Model. If dark matter has sub-GeV mass it will be produced in the hot Supernovae core and it will have sufficiently large … Read More

ABSTRACT: I will report on three areas of my research work on dark matter: indirect searches with gamma rays, primordial black holes as dark matter candidates and/or as dark matter generators, and dark matter kinetic re-coupling.

Dark matter that is capable of sufficiently heating a local region in a white dwarf will trigger runaway fusion and ignite a type 1a supernova. We consider dark matter (DM) candidates that heat through the production of high-energy standard model (SM) particles, and show that such particles will efficiently thermalize the white dwarf medium and ignite supernovae. Based on the … Read More

We consider an enlarged colour sector which solves the strong CP problem via new massless fermions. The spontaneous breaking of a unified colour group into QCD and another confining group provides a source of naturally large axion mass $m_a$ due to small size instantons. This extra source of axion mass respects automatically the alignment of the vacuum, ensuring a low-energy … Read More

Abstract: The lack of new physics discoveries has not only motivated increasing use of effective field theory (EFT) techniques to connect beyond Standard Model ideas and experiment, but also pushed us to think harder about how to interpret data and perform calculations within the EFT framework. As a result, progress has been made in at least two aspects: limitations and … Read More

Abstract: In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. In this talk, we will present a new framework: JUNIPR, “Jets from UNsupervised Interpretable PRobabilistic models”, which uses unsupervised learning to learn the intricate high-dimensional contours of the data upon which it is trained, without reference … Read More