Public version of code used in "Calorimetric Measurement of Multi-TeV Muons via Deep Regression" by Jan Kieseler, Giles C. Strong, Filippo Chiandotto, Tommaso Dorigo, & Lukas Layer, The European Physical Journal C volume 82, Article number: 79 (2022)
Please clone using:
git clone https://github.com/GilesStrong/calo_muon_regression.git
Providing one uses Conda for one's Python environments, a suitable Python environment can be built using:
conda env create -f environment.yml
This will create a new Conda environment called muon-regression, which can be activated using:
conda activate calo_muon_regression
Alternatively:
pip install -r requirements.txt
may be used to install the dependencies.
Self-hosted documentation, installation guide, and user guide are available by opening ./docs/build/html/index.html with a web-browser.
For GPU use (which is highly recommended), please separately install a suitable version of PyTorch based on your drivers.
Preprocessed datasets are available from https://doi.org/10.5281/zenodo.5163817. The example commands assume the data files are stored in ./data
Raw data may be made available at a later date, since exceeds the size limits for Zenodo.
Model training and evaluation can be accomplished by using the script in ./scripts/train_stride2_model.py, which takes a variety of options to produce different models. As an example, the following line will train the final model used in out paper:
cd scripts
python train_stride2_model.py train --gpu-id 0 --n-models 5 --extra-val-data cnn_hl_full_ensemble ../data/feb_calo_36_hl.hdf5
The results and model will be saved in ./results.
Alternatively, models can be trained within Jupyter Notebooks.
- Jan Kieseler
- Giles C. Strong
- Filippo Chiandotto
- Tommaso Dorigo
- Lukas Layer