NA6-LatticeHadrons: Lattice Hadrons

The network, spanning 34 partner research institutions, aims to embed existing lattice field theory expertise more deeply into the European hadron physics community, enhance access across the network to research expertise, data and new developments and exploit new links between research in lattice field theory, experimental and phenomenological hadron physics and high-performance computing and data analytics. Meeting these objectives will require the network:

  • To extend existing connections between researchers across Europe working in lattice hadron physics.
  • To develop new collaborations between European experimentalists and experts in lattice field theory computations
  • To link European research groups in lattice field theory to broader expertise in hadron phenomenology.
  • To connect experts in lattice computations and the methodology underlying these calculations with experts in other areas of large-scale computing and data analysis and management.
  • To exchange research insights between a broad range of researchers with diverse expertise via staff and student secondments and focused workshops.
  • To assess open research questions in hadron physics and develop new solutions by combining broad world-leading expertise across Europe and beyond.
  • To ensure the next generation of researchers using lattice field theory methods are well-connected to the research community in Europe. They will acquire transferable skills in a broad range of disciplines such as theoretical and experimental physics, high-performance computing and data analytics.
Work Package: 17
Lead beneficiary: TCD - Ireland
Spokespersons: This email address is being protected from spambots. You need JavaScript enabled to view it.
Partner: UREG - This email address is being protected from spambots. You need JavaScript enabled to view it., UAM This email address is being protected from spambots. You need JavaScript enabled to view it., UEDIN - This email address is being protected from spambots. You need JavaScript enabled to view it.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824093

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