Generator Reweighting

A repository for reweighting between different neutrino generators.

It works by training a BDT to discriminate between the two generators and then using the BDT to create weights for the events in the generator that we want to reweight to.

The weights are created using the likelihood ratio trick. E.g let $f(x)$ be a classifier trained with the binary cross entropy loss then the likelihood ratio is given by: $$w_x = \frac{f(x)}{1-f(x)}$$

To do

• interface model with nusystematics by creating a reweight engine - it should take 6 variables from an event [Enu_true, CosLep, ELep, EavAlt, Erec_bias_abs, Erec_bias_rel] and would produce a weight.
• investigate why particle multiplicities do not work with all FSI models, including ways of keeping Enu_true, CosLep, and ELep fixed while reweighting the other variables.

Getting Started

To clone this branch only:

git clone https://github.com/radiradev/generator_reweight_bdt

Environment setup

One should probably setup their own environment so they have full control, however as a quick way to run the code you can use this conda environment:

conda activate /afs/cern.ch/work/r/rradev/public/envs/pyg_gpu

LCG Views

• Add instructions

On lxplus we can source neccessary packages using an LCG view, however this uses an old version of scikit-learn without HistGradientBoostingClassifier:

source /cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/setup.sh

Basic Usage

It contains a top level script run.sh that accepts a .yaml file specied in /config it specifies the variables to reweight, the files for the nominal and target model and directories that would be used to save the files. The script first trains a BDT, then creates weights associated to the nominal file(s) and finally makes plots comparing the nominal, target and reweighted distributions. As an example:

bash run.sh hA_10a_to_hN2018.yaml

The scripts can also be ran individually (e.g to recreate plots or enforce some changes to weights).

Usage of individual scripts

You can also run the files indvidually, again you have to provide the config file for each python script.

Training

This script will train a BDT using the files specified in the .yaml config file and save the trained BDT in the trained_bdt\{nominal_name}_to_{target_name} directory. Where nominal_name and target_name are the names of the generators used to train the BDT specified in the .yaml config file.

python3 train.py hA_10a_to_hN2018.yaml

As a sidenote, in this step we can use any classifier that can discriminate between the two generators (e.g. a neural network, BDT, Random Forest, etc). The classifier currently used is Histogram-based BDT from sklearn.

Creating weights

This script will create weights for the files specified in the config corresponding to generator_a and will save the weights in the same directory as the trained BDT.

python3 reweight.py hA_10a_to_hN2018.yaml

Creating plots

This script will create plots using the weights created in the previous step. The plots will be saved in the plots\{nominal_name}_to_{target_name} directory. There is a config file in the config\plots.yaml which contains the settings for the plots.

python3 plot.py hA_10a_to_hN2018.yaml

Extra information

• Tutorial on BDT reweighting from the HEP Software Foundation.
• LBL presentation by Cristovao Vilela, and the repository that this work is based on.