Skip to main content

Library for neural importance sampling

Project description

MadNIS

Neural Multi-Channel Importance Sampling

Build Status Arxiv Code style: black pytorch

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/madgraph-ml/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"}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

madnis-0.1.3.tar.gz (29.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

madnis-0.1.3-py3-none-any.whl (32.8 kB view details)

Uploaded Python 3

File details

Details for the file madnis-0.1.3.tar.gz.

File metadata

  • Download URL: madnis-0.1.3.tar.gz
  • Upload date:
  • Size: 29.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for madnis-0.1.3.tar.gz
Algorithm Hash digest
SHA256 302915395f3b1967ace1b1f989676358d38a54ead634192c734f4c79bf9eea1d
MD5 13e83ed25374396609150c00ed094f66
BLAKE2b-256 733d0d3e1f413a46b1205adf0f76d596993e78c188f7aa806a21296b12be2028

See more details on using hashes here.

Provenance

The following attestation bundles were made for madnis-0.1.3.tar.gz:

Publisher: release.yml on madgraph-ml/madnis

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file madnis-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: madnis-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 32.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for madnis-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 e6e27239e067716295a6c87e03445b1457dea4e3ead9e4eefc33a48d0c5f471f
MD5 2404e1856cb669468485af8a18f7fca9
BLAKE2b-256 2d38c559dc8adf5ee0c553ee832e8692617e4392c4f8bb267fdfe1beedcd9742

See more details on using hashes here.

Provenance

The following attestation bundles were made for madnis-0.1.3-py3-none-any.whl:

Publisher: release.yml on madgraph-ml/madnis

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page