Precision tests of the Standard Model at colliders: Quantum Field Theory at work (QuantumFronTiers)

Elementary particles experiments can help us to answer several open questions about the structure of our Universe, like the presence of dark matter and dark energy, or the strong asymmetry between the observed amount of matter and antimatter. The upcoming generation of particle colliders will deliver an unprecedented level of experimental precision, with uncertainties reduced by up to two orders of magnitude in key measurements. These results open extraordinary opportunities to probe fundamental physics — but only if the theoretical predictions can match this new standard of accuracy. At present, however, the theoretical framework required to interpret and exploit such high-quality data is not yet available at the necessary precision level. Major challenges in theoretical physics and mathematics have so far hindered our ability to compute the missing corrections essential for this task. Bridging this gap is critical: without a comparable leap in theoretical tools, the full scientific potential of future collider experiments risks being severely undermined. The proposal addresses this pressing need to advance the frontier of theoretical calculations and ensure that theory and experiment evolve in step toward new discoveries, identifying three major goals: - Development of a new methodology, combining recent mathematical and computational advances authored by the PI, to achieve a simple representation of the scattering amplitude in quantum field theory, applicable in automated form to an arbitrary scattering process. - Prediction of the cross sections of a few benchmark processes at hadron and lepton colliders, including the full set of second-order quantum corrections, reaching the precision level necessary to interpret the most precise experimental data. - Implementation of a framework to determine with high precision the fundamental parameters of the Standard Model, our theoretical reference, and to interpret any deviation from the data of its predictions.