Neural Simulation-Based Inference Workflow demonstration

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Table of Contents

• Setup
• Introduction
• Library
• License

Setup

We will use pixi to setup the environment for the workflow. The specifications are defined in the pixi.toml file. If pixi is not installed on your machine follow the instructions in pixi seutp guide. Then proceed to install the environment with:

pixi install -e nsbi-env-gpu

Currently the environment can only be built on machines with GPU.

A jupyter kernel can then be created by running:

pixi run -e nsbi-env-gpu python -m ipykernel install --user --name nsbi-env-gpu --display-name "Python (pixi: nsbi-env-gpu)"

Introduction

The notebooks presented here aim to serve as a guide to use the nsbi-common-utils library being developed (currently not deployed to PyPi). The core model and workflow is related to the NSBI analysis recently published by ATLAS:

• An implementation of neural simulation-based inference for parameter estimation in ATLAS (https://arxiv.org/pdf/2412.01600)

• Measurement of off-shell Higgs boson production in the $H\to ZZ \to 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV p-p collisions with the ATLAS detector (https://arxiv.org/pdf/2412.01548)

We demonstrate the usage of nsbi-common-utils applied to a full-scale LHC analysis. The physics results presented in the notebooks only serve as examples of the workflow. The code in this tutorial is partially derived from the original ATLAS analysis code written by Jay Sandesara [git], R.D. Schaffer [git] and Arnaud Maury [git].

The workflow currently uses the ttbar open data released by CMS, and the Higgs to tau tau dataset from FAIR universe challenge. More open datasets will be added in the future.

Library

To use the library nsbi_common_utils developed here in general cases outside of this tutorial, do:

python -m pip install --upgrade 'nsbi-common-utils @ git+https://github.com/iris-hep/NSBI-workflow-tutorial.git'

The library tools are steered by a configuration file - an example can be found in the FAIR Universe Challenge workflow config.yml.

Workflow bluprint (tentative):

Acknowledgements

This work is being supported by the U.S. National Science Foundation (NSF) cooperative agreements OAC-1836650 and PHY-2323298 (IRIS-HEP).

License

nsbi-common-utils is distributed under the terms of the MIT license.