Semileptonic B-meson decays proceeding via b→c and b→u transitions are processes widely studied in the Standard Model (SM) of Particle Physics. Measurements from B factories and the LHC have been used to determine |Vcb| and |Vub|, two of the elements of the Cabibbo–Kobayashi–Maskawa matrix and, as such, crucial input parameters of the SM. There is a long-standing tension affecting both |Vcb| and |Vub| determinations from inclusive and exclusive decays and significant hints of lepton flavour universality violation in semileptonic B meson decays with tau leptons in the final state and rare decays mediated by the b→s transition. Their possible origin from New Physics (NP) beyond the SM has been broadly scrutinized in the context of exclusive decays. However inclusive B meson decays have never been used as competitive probes of NP. In this project we will establish the theoretical foundation for the study of non-standard interactions in inclusive semileptonic decays driven by b→c and b→u transitions. We will perform a comprehensive model-independent analysis of the constraints on NP from this class of decays, including for the first time all available data on kinematic differential distributions. We will investigate the implications for viable scenarios of physics beyond the SM and present their interplay with the exclusive modes. Disentangling low-energy non-perturbative effects from NP effects is challenging and requires addressing new issues, making the proposed action interdisciplinary and innovative. The researcher Matteo Fael has a broad experience in the theory and phenomenology of inclusive B-meson decays while the supervisor Gian Francesco Giudice is a world-class expert in flavour and collider phenomenology, development of NP models and study of their implications for particle physics and cosmology.

With Deep Learning becoming ubiquitous in our life, running Deep Learning algorithms in real time on an heterogeneous set of hardware platforms is a pressing need in many aspects of our society. While traditional workflows based on standard CPUs and GPUs are established, Deep Learning inference on low-power devices (e.g., cars, smart phones, watches, etc) is gaining more attention. Typically, this would require strong background in electronic engineering to convert a neural network into a Digital Signal Processor. We propose to develop a complete open-software library to automatically convert Deep Neural Networks to electronic circuits, using High Level Synthesis tools. With a large basis of potential applications (e.g., autonomous cars, medical devices, portable monitoring devices, custom electronics as in the real-time data processing system of large-scale scientific experiments, etc.), the hls4ml library would assists users by automatising the logic circuit design as well as by reducing resource utilisation while preserving accuracy.