Neural Multi-Channel Importance Sampling
MadNIS is a Python library for neural multi-channel importance sampling based on PyTorch. It will be used for Monte Carlo LHC event generation in future versions of MadGraph. This repository provides the MadNIS code as a stand-alone library that can be applied to arbitrary Monte Carlo integration and importance sampling tasks.
This repository contains a refactored version of the code used in our publication The MadNIS reloaded. It is still under active development and will receive frequent updates and bugfixes.
The documentation of the madnis package can be found under docs.madnis.ai.
Installation
You can either install the latest release using pip
pip install madnis
or clone the repository and install the package in dev mode
# clone the repository
git clone https://github.com/MadGraphTeam/madnis.git
# then install in dev mode
cd madnis
pip install --editable .
Citation
If you use this code or parts of it, please cite:
@article{Heimel:2023ngj,
author = "Heimel, Theo and Huetsch, Nathan and Maltoni, Fabio and Mattelaer, Olivier and Plehn, Tilman and Winterhalder, Ramon",
title = "{The MadNIS reloaded}",
eprint = "2311.01548",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "IRMP-CP3-23-56, MCNET-23-12",
doi = "10.21468/SciPostPhys.17.1.023",
journal = "SciPost Phys.",
volume = "17",
number = "1",
pages = "023",
year = "2024"}
Metadata
Release files for madnis 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| madnis-1.0.0.tar.gz | 38.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| madnis-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 82.2 kB
Release files / madnis-1.0.0.tar.gz
| Download URL | madnis-1.0.0.tar.gz |
|---|---|
| Size | 38.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
98e1b04703dc0ebd90de52c29393b6438c20ef15c2332631079aaec807d6b133
|
|
BLAKE2b-256 checksum How to use checksums |
7d5786bba81e1abd7c05ba9472712b789203ad43c8227522f5b0e77b343e09d1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Mar 27, 2026.
Transparency logRelease files / madnis-1.0.0-py3-none-any.whl
| Download URL | madnis-1.0.0-py3-none-any.whl |
|---|---|
| Size | 43.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
816ecef5aa8f3f010f897b5d07189680089e0d0f13eac506de0163e4939b4d7a
|
|
BLAKE2b-256 checksum How to use checksums |
e351ec830dbed447e9e2255ce99f8a56722ab395035c84d3f76e0f1b8d319777
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Mar 27, 2026.
Transparency log