Les Houches

Aims:

• 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.