REAL: Reweighting Events using Adaptive Learning

REAL improves the modeling of jet to τ_h backgrounds by leveraging advanced machine learning (ML) techniques such as Boosted Decision Trees (BDTs). By addressing the limitations of traditional fake factor methods, REAL enables more accurate multi-dimensional reweighting and background estimation.

Features

• Adaptive Reweighting: Uses ML techniques to reweight a high-dimensional dataset, mapping events that fail a tau ID onto those that pass the tau ID.
• Jet to τ_h Fake Factors: Focuses on improving modeling for jet to τ_h backgrounds.
• Generalization: Plans to expand to all particle misidentification rates in future iterations.

Installation and Setup

Clone the repository:

git clone https://github.com/IreneAndreou/REAL.git
cd REAL

Use the provided environment.yml file to create and activate the environment:

conda env create -f environment.yml

Activate the environment:

conda activate real