🎉 Welcome to MadLAD
An all‑in‑one event‑generation wrapper for particle physics.
MadLAD orchestrates the full chain from matrix‑element (ME) calculation to detector simulation.
It is built on top of the following well‑known tools: MadGraph51, Pythia82, LHAPDF3, FastJet4 and Delphes5. All of these components are built into Docker or Singularity containers, so you can run MadLAD on any machine that supports one of those container runtimes.
Contributing – We love community input!
If you’d like to add a new process, improve documentation, or fix a bug, just fork the repo and submit a pull request.
🚀 Quick Start
1️⃣ Build a Container
python -m madlad.build -c examples/config_build.yaml
Requires Docker or Singularity to be installed.
The config_build.yaml file lets you pin the exact software versions you need.
2️⃣ Generate Events
python -m madlad.generate --config-name=ttbar-allhad
Feel free to drop your own process YAML files into the processes/ directory and use them in the same way.
📚 References
| # | Citation |
|---|---|
| 1 | J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP 1407:1‑157 (2014). |
| 2 | T. Sjöstrand et al., “An introduction to PYTHIA 8.2,” Comput. Phys. Commun. 191 (2015) 159‑177. |
| 3 | A. Buckley et al., “LHAPDF6: parton density access in the LHC precision era,” Eur. Phys. J. C75 (2015) 1‑20. |
| 4 | M. Cacciari, G.P. Salam & G. Soyez, “FastJet user manual: (for version 3.0.2),” Eur. Phys. J. C72 (2012) 1‑54. |
| 5 | J. de Favereau et al., “DELPHES 3: a modular framework for fast simulation of a generic collider experiment,” JHEP 1402 (2014) 1‑26. |