NLPResum

NLPResum

Progress in particle physics relies to a large extent on our ability to deal with kinematic limits of scattering processes. I propose to develop innovative methods, that will allow physicists to make precise predictions in these limits and thus reach a deeper comprehension of particle scattering as a whole. This topic is very important both from a theoretical and a phenomenological point of view. On the phenomenology side I propose to develop resummation of next-to-leading power (NLP) logarithms from quantum Chromodynamics (QCD), at threshold and at small transverse momentum, based on my previous studies concerning the factorization of these logarithms. The achievement of this goal will allow the community of theoretical physicists to produce precise predictions, which will be essential to spot deviations in the experimental data, possibly due to new physics. I will apply resummation of QCD NLP logarithms to several electroweak annihilation processes and top quark pair production, comparing with data collected at the LHC, thus producing a comprehensive study of the electroweak sector of the Standard Model at unprecedented accuracy. On the theoretical side, I propose to study the high-energy limit of particle scattering. Here, I will use innovative methods such as the shockwave formalism, which I developed with collaborators. I will determine the factorization structure of amplitudes with two and more particles in the final state. Based on these factorisation theorems I will calculate those amplitudes to all orders in closed form, which will enable the resummation of high-energy logarithms. Furthermore, I will bootstrap these results to obtain the structure of infrared divergences of gauge theory scattering amplitudes in general kinematics, at four loops and above. Last, I will exploit all-orders results in the high-energy limit to produce the most accurate predictions for inclusive two-jet production and Higgs production in association with two jets.


Leonardo Vernazza – INFN TORINO

Fellow

Leonardo Vernazza

Date

31 July 2020

Tags

FIRST CALL
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This project receives funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Cofund Action, grant agreement N° 754496.