Periodic Reporting for period 1 - ParDHonS_FFs.TMDs (Parton Dynamics in QCD Hadron Structure: collinear FFs and unpolarized TMDs)
Reporting period: 2018-09-01 to 2020-08-31
Nucleons are probed in scattering experiments whereby they are bombarded with high energy beams made of other particles (leptons or protons). The analysis of the remnants of these collisions allow scientists to derive information on the distribution of quarks and gluons in protons. This information is encapsulated in Parton Distribution Functions (PDFs) and in Fragmentation Functions (FFs). They encode the probability that a given quark or gluon carrying a given fraction of the parent nucleon’s momentum is struck from the incoming or outgoing nucleon, respectively.
This Action aimed at achieving ultimate precision of PDFs and FFs, possibly to match the requirements for discovery at current colliders. One of such colliders, the Large Hadron Collider, is the most powerful particle accelerator ever built by mankind. The determination of PDFs and FFs cannot be reliably achieved from first principles. Instead, they are modelled by means of some parametrisation, which is then optimised by comparing the PDF-dependent prediction of a variety of hadronic cross sections to their actual measurements, a procedure that is called (global) QCD analysis (or fit). In this sense, PDF/FF determinations can be labelled generally as a nonlinear regression problem, whereby one has to learn a set of functions from data. This goal was achieved by developing and utilising innovative artificial intelligence and machine learning techniques, an essential part of the research plan. These techniques allow one to achieve a statistically sound and faithful representation of PDF/FF uncertainties, a feature which is crucial for a characterisation of the SM and for discoveries beyond it.
Concerning objective 1), three complementary pieces of software were developed to realise the fast computation of the hadronic observables used to extract FFs: APFEL (https://apfel.hepforge.org/ download.html) APFEL++ (https://github.com/vbertone/apfelxx) and NangaParbat (https:// github.com/vbertone/NangaParbat). A global determination of collinear FFs of charged pions and kaons from all available data will be released by the end of 2020. Concerning objective 2) the ER developed PineAPPL, a library that produces fast-interpolation grids of physical cross sections, computed with a general-purpose Monte Carlo generator, accurate to fixed order in the strong, electroweak, and combined strong–electroweak couplings. The PineAPPL library is publicly available from the following link: https://zenodo.org/record/3992765. Furthermore, the ER devised a method to characterise theory uncertainties in PDF fits, and applied it to nuclear uncertainties and to missing higher-order uncertainties. Finally, the ER studied the impact of single-top t-channel, single- and di-jet production and charm-tagged neutrino DIS on PDFs. This work led to three publications. PDF sets incorporating these data sets are publicly available from the NNPDF web page (http://nnpdf.mi.infn.it).
All the available results produced as part of this Action are available in terms of publicly available scientific publications and pieces of software, as outlined above. Results have been disseminated through eleven talks delivered at international conferences and workshop. Due to the Covid pandemic, however, these had to be drastically limited in 2020. Outreach and public engagement activities have also been pursued as complementary activities to the dissemination of results. In particular, the ER has joined the 2019 Nikhef Open Day, a one-day event that proposes scientific popularisation for children and young adults, and to the 2019 Nikhef final-year Jamboree, a two-day event that showcase, in an accessible and informal way, the various research lines currently pursued at Nikhef.
Because this Action focused on fundamental research, it has no tangible and immediate impact on economy and society. However, because of the high inter-disciplinarity of the advanced statistical and computational techniques developed in this Action, some indirect returns are foreseeable in the future. In particular, the relationship between the way in which the problem tackled in this Action was addressed is intertwined with data science, one of the big changers in the current socio-economic framework.