• General information about setting up the cms area, if wanting to do so.

The first thing to do is: source setup.sh. This appends the appropriate pacakages to your PYTHONPATH. It also loads a cms environment; you may remove this line if already using a package manager like conda.

To separate the VBF or ggH HToEE signal from the dominant Drell-Yan background, we may use a Boosted Decision Tree (BDT). For this, you may use the training/train_bdt.py script. This requires a configuration file -c bdt_config_<vbf/ggh>.yaml which specifies:

• whether we are training on ggH or VBF processes
• the sample options (files dirs and names, tree names, years, ...)
• variables to train our BDT with
• preselection for all samples.

An example config for VBF training can be found here: bdt_config_vbf.yaml

The command to train a simple VBF vs Bkg BDT with a train-test splitting fraction of 0.7, is:

python training/train_bdt.py -c configs/bdt_config_vbf.yaml -t 0.7

You will notice that the performance training this simple BDT is poor; the classifier predicts only the background class, due to the massive class imbalance. To remedy this, try training with the sum of signal weights increased such that they equal the sum of background weights, by adding the option -w.

To optimise the model hyper parameters (HP), use the option -o. This submits ~6000 jobs to the IC computing cluster, one for each hyper parameter configuration. If you want to change the HP space, modify the BDT() class constructor inside HToEEML.py. Each model is cross validated; the number of folds can be specified with the -k option. Note that to keep the optimisation fair, you should not run with the -r option, since this re-loads and reshuffles the dataset; we want each model to use the same sample set.

Once the best model has been found, you may train/test on the full sample by running with option -b. This will produce plots of the ROC score for the train and test set, and save it in the working directory in a plots folder. The output score for the two classes is also plot.

To apply a rewighting of a given MC sample (Drell-Yan as default) to data, in bins of pT(ee), use the option -P. This is motivated by known disagreement between Drell-Yan simulation and data. The reweighting is derived in a dielectron mass control region around the Z-mass, before being applied in the signal region. An option to reweight double differentially in bins of jet multiplicity may also be configured in the training script.

An extra word of warning on this - reading in the control region to derive the rewighting requires much more memory than the nominal training. Therefore its best to submit the training to the IC batch, first time of running. Once the re-weighted dataframe has been saved once, it should be fine to run locally again.

To use an LSTM neural network to perform separate VBF signal from background, we use the script training/train_lstm.py. This takes a separate config, similar to the BDT, but with a nested list of low-level variables to be used in the LSTM layers. High level variables are also used, interfaced with the outputs of the LSTM units in fully connected layers. For an example config, see: configs/lstm_config.yaml.

The training can be run with the same syntax used in the BDT training. For example, to train a simple LSTM with default architecture and equalised class weights:

python training/train_lstm.py -c configs/lstm_config_vbf.yaml -t 0.7 -w

To optimise network hyper paramters, add the -o option. This may take up to a day per job, since we perform a k-fold cross validation. Following the optimisation, if all jobs have finished, add -b to train with the best model and produce ROC plots for the classifier.

Once a model has been trained and optimised, you may use the output score to construct categories targeting ggH/VBF production. This is done by splitting the signal + background events into boundaries defined by the output classifier score, such that the average median significance (AMS) is optimised. To perform the optimisation, which also optimises the number of categories (defaults to between 1 and 4) the following command can be run:

python categoryOpt/bdt_category_opt.py -c configs/bdt_config_ggh.yaml -m models/ggH_BDT_best_clf.pickle.dat -d

which runs an optimisation for ggH categories. Note that the -d option replaces background simulated samples with data. The -m option specified the pre-trained BDT; in this case we use the best model from earlier HP optimisation.If wanting to use the classifier from a pT(ee) reweighted sample, be aware that the model name will be different to the nominal models.

To produce the trees with the category information needed for final fits, we use categoryOpt/make_tag_sequence.py. This takes a config specifying the BDT boundaries for each set of tags (see configs/bdt_boundaries_config.yaml for an example). The script also requires an additional config with sample and training varaible info (see configs/tag_seq_config.yaml for info an example)

• assert cut string vars are in nominal vars
• break plotting class up to avoid duplicating code