GRANIITTI

https://arxiv.org/abs/2304.06010
https://arxiv.org/abs/1910.06300

https://indico.cern.ch/event/1148802/contributions/5004853 -- Diffraction and Low-x 2022 talk

Algorithmic Engine and Monte Carlo Event Generator for High Energy Diffraction

See VERSION.json for the latest version information.

Physics introduction

See the page: https://mieskolainen.github.io

Installation

1. Pull the repository

git clone --depth 1 https://github.com/mieskolainen/graniitti && cd graniitti

2. Environment setup

Standalone Ubuntu

sudo apt install cmake g++ python3-dev curl

CERN lxplus (CVMFS) environment

source /cvmfs/sft.cern.ch/lcg/views/setupViews.sh LCG_98python3 x86_64-centos7-gcc9-opt

Conda environment with C++ compilation and Python analysis tools

wget https://repo.anaconda.com/archive/Anaconda3-2023.03-Linux-x86_64.sh
chmod +x and execute the installer

conda env create -f environment.yml
conda activate graniitti
pip install -r requirements.txt

3. Autoinstall IO-format dependencies

cd install && source autoinstall.sh && cd ..

4. Set environment variables and compile the generator C++ code

source install/setenv.sh
make -j4

First run

Set environment variables, then execute the main generator program

source install/setenv.sh
./bin/gr

See /docs/FAQ for more information.

Event generation

Simulate MC events

./bin/gr -i gencard/STAR_1792394_pipi.json -w true -l true -n 50000

Analysis

(Python) Analyze MC and data

python python/iceshot --hepmc3 STAR_1792394_pipi --hepdata dataset_STAR_1792394_pipi --pid '[[211,-211]]' --cuts STAR_none

(Python) Compare MC with differential fiducial measurements made at RHIC/Tevatron/LHC

pytest tests/testbench_STAR_1792394.py -s --POMLOOP true
pytest tests/testbench_exloop.py -s --POMLOOP true

(Python) MC model tuning via HPC-distributed Bayesian / evolutionary optimization

ray start --head --temp-dir=/tmp/ray
python python/icetune --tuneset default

For C++ (ROOT) based analysis tools, see /docs/FAQ and examples under /tests.

Code quality assurance

Unit and integration tests

make -j4 TEST=TRUE && ./bin/testbench*
pytest tests/testbench_*.py -s

Reference

If you use this work in your research, please cite the paper:

@article{mieskolainen2019graniitti,
    title={GRANIITTI: A Monte Carlo Event Generator for High Energy Diffraction},
    author={Mikael Mieskolainen},
    year={2019},
    journal={arXiv:1910.06300},
    eprint={1910.06300},
    archivePrefix={arXiv},
    primaryClass={hep-ph}
}

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graniitti's Introduction

GRANIITTI

Algorithmic Engine and Monte Carlo Event Generator for High Energy Diffraction

Physics introduction

Installation

1. Pull the repository

2. Environment setup

3. Autoinstall IO-format dependencies

4. Set environment variables and compile the generator C++ code

First run

Event generation

Analysis

Code quality assurance

Reference

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