• List of Participants (Accessible only to resgistered participants)
  • Present in Les Houches in session 1
  • Present in Les Houches in session 2
  • The Global List including all registered participants for Tools and MC

• intro talk: PDF

• ideas before LH

• summary TH: final_th_merged.pdf

• Jet activity in VBF Z and VBF W events
• MC variation ("compendium")
• MC variation ("case study")
• ttbar+bbar (2 Nb jets)
• gg -> HZ pheno study
• self consistency of ISR in showers
• dealing with negative weights
• differential EW corrections for ttW

• Ideas before LH

Please add your name to the projects you are interested in contributing to:

• Likelihood construction and applications
• Recasting/fastsim tool & method comparisons
• Search/measurement compatibility
• Reinterpretation tool interoperability
• Long-lived particle recasting
• Analysis description languages
• Correlations between signal regions of analyses

If you are interested in a session on learning to install and run Contur please sign up here and we will schedule something early in the week.

All welcome to contribute: https://www.overleaf.com/read/jzsrmbqffwtb

….this should actually become a short review of what material is provided by different experimental groups , how it is used in recasting, how it improves things, additional aspects to consider, etc…..cover searches as well as measurements……

Extra angles to include: use of event sharing from/between experiments and pheno? Analysis combinations by experiments on elementary nuisances: provide multi-analysis SL covariance.

• Public CMS software code repository: https://github.com/cms-sw/cmssw
• CMS open data portal: http://opendata.cern.ch/docs/about-cms

=== Library of regression and classification networks Keras/Tensorflow and Scikit-learn to start Library of Training data, maybe connected to hep-data (database)

Train regression on the following: - electroweak cross-section in pMSSM19 (DeepXs, …) - cross sections Wprime, Zprime (how many dimensions?) - Doublet Higgs model (6 dim)

Train likelihoods: - global fits Gambit Xenoda data (train on likehoods on different observables, e.g. only colliderbit) - Higgs model with many nuisance parameters (100?)

Train on observables: - example relic density

Train on reconstruction efficiencies: - LLSps

(want to share code for the networks…)

Output: - git repositories for networks - Xenodo data - talk with hepdata - paper - prototype code for the network library

Other info: - How to sample ? Depends on goal, proposed active learning etc.