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  • Reconstruction of V0s, kinks & prongs:

    • objective: The reconstruction of in-flight decays incl. V0s, kinks, prongs etc is a special strength of ILD due to its TPC. The current reconstruction exploits this strength only in a very rudimentary fashion. Realiable finding and constrained fitting of such decays will improve the overall reconstruction of jets as well as enable searches for exotic long-lived particles, i.e. from Dark Sector models.

    • tools & methodology: full ILD simulation, review, unify and improve treatment of in-flight decays before and after particle flow. Exploit dE/dx for PID, develop constrained fitting.
    • contact: Daniel Jeans, Graham Wilson, Jenny List

    Particle Flow improvement: fragment classification

  • objective: 

  • tools & methodology: 
  • contact: Graham

  • Wilson

    Photon Reconstruction:

    • objective: Reconstructed well calibrated and unbiased estimates of photon 4-vectors with understood measurement uncertainties are essential for more sophisticated uses of photons such as in brems recovery for leptons, mass-constrained fits, pi0 reconstruction, jet error parametrisation etc. Understand and exploit the full potential of a highly-granular ECal, and of the continuous tracking (photon conversions) of ILD.

    • tools & methodology: full ILD simulation, review and revise photon calibrations and error estimates apply to cases listed above.
    • contact: Graham Wilson, Daniel Jeans

  • Jet clustering with PFO uncertainties:

    • objective: For final-states with more than 2 jets, usually the jet clustering mistakes dominate the JER. Jet algorithms used in ILD are mainly the ones developed for LEP (with the exception of the Valenica algorithm). None of them exploits the full information provided by a particle flow detector, which includes reliable uncertainty estimates for each PFO (aka ErrorFlow) - how can this information be used in jet clustering?

    • tools & methodology: Either "classically": develop new distance measures and recombination schemes which take into account ErrorFlow information - or employ machine learning!
    • contact: Marcel Vos, Jenny List

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