Code: cres

• Apply cell resampling (paper 1 paper 2 source binaries) to Higgs signal/background (closure tests, measure of simplification e.g. #event reduction)
• Graph of fraction of negative weights for Higgs processes/background (slide 5/9 Jeppe talk)

• di-photon NLO sample (m_gamgam centred around Higgs mass window)

• Discussion on good metrics (what to compare before considering events close)
• Measure the impact on reals vs virtuals, which events are most likely to be altered and by how much?
• Try it on proper experimental samples (what metric is good, what issues arise?)
• Applying this to NNLO (e.g. HighTea samples)
• Try e.g. jpsi→leptons something very narrow (technical issues regarding IR sensitivity, modifying distributions…)
• Plots of mean/median/width of the cell resampling bins, studying these distributions and their potential impact
• Can narrow weight distribution for more efficient event unweighting

• Q: why is w+5 improving more than z+3?
  1. initial event sample size? (no, both have 1e9)
  2. dipole cut vs improvement? (check dipole cut used)
• Q: calculating density of events to “pre-check” that this algorithm may work?
• Q: metric needs to be compatible with how ps generator is populating ps, proof that this is not biasing anything?
• Q: should there be a bias towards positive values (since you iteratively add nearest event until wi>=0)?
  1. ps generator dependent because this alters which events are clustered, can this have an impact on the physics?
• Q: plotting statistical uncertainties on the original sample
  1. more easily allows verifying that differences are within statistical uncertainty
• Q: on which type of event does this have the biggest impact?
  1. imagine reals are more impacted than virtual
  2. we were completely agnostic
• Q: can we really prove that this does not alter distributions? (prove that you preserve distributional structure of the observables you compute)
  1. we do not alter any of the event kinematics
  2. there is no cross talk of events separated by more than the maximum allowed distance
  3. IRC safe measure important, but this is not a sufficient condition, can you reproduce infrared sensitive observables (e.g. Sudakov shoulder, 0-bin of ptZ), can your smearing reproduce this feature in the limit that the smearing goes to 0?
• Q: mean/median/width of the cell resampling bins
  1. we have plots that we can examine