I've added some conveniences in the first two commits, so I don't have to change them myself every time I pull from the master. These conveniences include a hard coded script, which moves my control plots to the afs, so I can upload them to my CERN website, a colour styling choice for a certain signal sample, adding a control variable group and process-settings group and comment the HH->bbtautau signal sample out.
The third commit changes the definition of the m_bb-variable for the boosted HbbJets, so I can use this variable in the inference model.
The fourth commit adds the vbf signal output node to the machine learning model, adds the vbf category to the ml categorization and I've created three new ml-variables, that target the vbf channel. These variables are the delta eta between two Jets from the VBFJet-Collection from the selection, the invariant mass of these jets and a simple tag that checks if there are at least two VBFJets for the event or not.
I've added some conveniences in the first two commits, so I don't have to change them myself every time I pull from the master. These conveniences include a hard coded script, which moves my control plots to the afs, so I can upload them to my CERN website, a colour styling choice for a certain signal sample, adding a control variable group and process-settings group and comment the HH->bbtautau signal sample out.
The third commit changes the definition of the m_bb-variable for the boosted HbbJets, so I can use this variable in the inference model.
The fourth commit adds the vbf signal output node to the machine learning model, adds the vbf category to the ml categorization and I've created three new ml-variables, that target the vbf channel. These variables are the delta eta between two Jets from the VBFJet-Collection from the selection, the invariant mass of these jets and a simple tag that checks if there are at least two VBFJets for the event or not.