A first phase (12 months) was devoted to setting up the project. A project assistant was recruited starting on day 1. A team of five was recruited, including two postdocs, two Ph.D. students, and an assistant professor.
Major preliminary work was accomplished by the PI: a state-of-the-art determination of the parton distribution functions (PDFs) that encode proton structure with current artificial intelligence-based, but not yet ML-based methodology.
The second phase (12 months) started when the initial team of five joined the PI. The team has since met once a week, with two more weekly meetings held jointly with the NNPDF collaboration led by the PI (
http://nnpdf.mi.infn.it/(se abrirá en una nueva ventana)). In-person meetings of the team with NNPDF were held in September 2018 in Gargnano (Italy), and in Amsterdam in February 2019. Two major results were obtained: 1) the initial proposal of a ML-based methodology for PDF determination, based on the automatic optimization of the methodology itself; 2) the development of a systematic methodology for the inclusion of theoretical uncertainties in PDF determination.
In the third phase (12 months), the team reached its full size, with two postdocs and a PhD student joining at the beginning of year 3. Weekly meetings continued remotely after the beginning of the covid pandemic in February 2020. In-person meetings of the team and NNPDF took place in Varenna (Italy) in end-August 2019 and in Amsterdam in February 2020. Two milestones were achieved: 1) the full implementation and validation of the aforementioned ML-based methodology for PDF determination; 2) the full implementation of a methodology for the simultaneous inclusion of QCD and electroweak corrections, i.e. a state-of-the-art theoretical description of the data. A wide variety of spillover results was obtained, involving public codes for data science tasks (such as hardware acceleration and numerical integration).
During the fourth phase (15 months) the main goal was achieved: an open-access code for PDF determination based on ML, and the construction of a first PDF set based on it. Because of the pandemic, tasks requiring more intensive face-to-face discussion were postponed and work was concentrated on tasks amenable to remote working, such as computer coding. The meeting foreseen in Gargnano in September 2020 was canceled and no winter meeting was held in 2021. Summer meetings were resumed in Gargnano in August 2021. A PhD student and postdoc left the project at the end of year 4. A six-month extension of the project was granted due to the pandemic.
During the final phase (15 months) a first showcase of the main result of the project was accomplished: the discovery of first evidence for an intrinsic charm component of the proton. The two ancillary developments of the project were also accomplished, i.e. the development of codes that allow for the inclusion of theoretical uncertainties and electroweak corrections. An in-person meeting scheduled in Amsterdam for February 2022 was moved to April due to covid, a Summer meeting was held in September 2022 in Gargnano and a final winter meeting in Amsterdam in February 2023. A PhD student and a postdoc left six months before the end of the project, with one postdoc and one PhD student remaining until the end.
Besides publication in scientific papers, presentations at conferences at workshops, and accessibility in public repositories, all results of the project have been made public in the form of open-source computer code with extensive documentation and compliant with the FAIR principles. A first summer school on "advanced artificial intelligence for high-energy physics", based on the use of this code, will be held in July 2023
https://aiep.lakecomoschool.org/(se abrirá en una nueva ventana)
