LIEDER : Long lIvEd Displaced jEt classifieR

First draft of a Fully Connected Neural Network aiming at event-wise binary classification (signal vs background) for searches for long lived particles with CMS detector.

Currently implemented in keras.

Input and output root files are designed to be compatible with LLP repository.

Preliminary naf gpu setup

See: https://confluence.desy.de/pages/viewpage.action?spaceKey=UHHML&title=Using+GPUs+in+naf

Prepare folders

mkdir dataframes
mkdir model_weights
mkdir root_files

write_pd_skim.py

It reads input root files and transforms them into h5 files.

samples.py

Same as LLP repo; used to load sample names.

dnn_functions.py

Simple function to draw training and validation losses and accuracies.

dnn.py

• Split training/test samples for both signal and background
• Transform them into h5
• Training function
• Calculate performances
• Write the output scores of test samples
• Convert h5 back to root files, compatible with any macro of LLP repository