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--- | ||
title: "Implementing AD in CMS Combine" | ||
layout: post | ||
excerpt: "A CERN Summer Student 2025 project aiming at the integration of | ||
automatic differentiation (AD) into the CMS Combine tool to accelerate statistical | ||
inference by leveraging RooFit's AD support and LLVM-based gradient generation." | ||
sitemap: false | ||
author: Galin Bistrev | ||
permalink: blogs/2025_galin_bistrev_introduction_blog/ | ||
banner_image: /images/blog/gsoc-banner.png | ||
date: 2025-07-05 | ||
tags: cern cms root combine c++ rooFit automatic-differentiation | ||
--- | ||
|
||
### Introduction | ||
Greetings! I’m Galin Bistrev, a fourth-year student specializing in Nuclear and | ||
Particle Physics at the University of Sofia "St. Kliment Ohridski". | ||
As part of the CERN Summer Student Programme 2025, I’m working on a project to | ||
integrate automatic differentiation (AD) | ||
into the CMS Combine tool. | ||
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### Project description | ||
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||
This project focuses on implementing automatic differentiation (AD) into the CMS | ||
Combine tool, which the primary statistical analysis framework used by the CMS experiment | ||
at CERN. Combine is built on top of RooFit, which recently introduced AD | ||
technology to support minimization methods. | ||
By generating computationally efficient gradients through AD, RooFit enables significant | ||
performance gains. RooFit’s implementation of AD works by converting internal | ||
likelihood representations into standalone C++ code, from which gradient code is | ||
produced. This approach not only accelerates fitting but also improves | ||
the portability and shareability of likelihood models, making them accessible | ||
even to users without deep knowledge of RooFit or Combine internals. | ||
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### Brief description of the CMS Combine engine | ||
Combine is a statistical analysis tool designed to compare a model of expected | ||
observations with real data. It’s commonly used for tasks such as discovering | ||
new particles or processes, setting limits on potential new physics, and measuring | ||
physical quantities like cross sections.While Combine was developed with High | ||
Energy Physics (HEP) applications in mind, it contains no built-in physics | ||
knowledge,thus it remains completely general and independent of the interpretation | ||
of any specific analysis. This flexibility allows it to be used across a wide range | ||
of statistical problems | ||
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### Project goals | ||
The main goals of this project are: | ||
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- Support external users in working with Combine-generated models without needing | ||
to dive into RooFit or Combine internals. | ||
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- Optimize performance in high-complexity workflows within the Combine framework. | ||
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### Implementation strategy | ||
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- Refactor Combine to use standard RooFit primitives where possible, and add AD support to custom components. | ||
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- Integrate RooFit's AD- generated gradients into Combines's likelihood | ||
evaluation and minimization workflows to improve performance and enable external | ||
gradient use. | ||
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- Developing benchmarks to quantitatively demonstrate the performance improvements AD brings to Combine overflows. | ||
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##Conclusion | ||
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By integrating automatic differentiation into the CMS Combine tool, we aim to make | ||
statistical inference not only faster but also more accessible to the broader | ||
community. I’m excited to contribute to this effort during my time at CERN and | ||
look forward to sharing progress, insights, and benchmarks as the project evolves. | ||
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### Related Links | ||
- [CMS Combine GitHub page]https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/latest/ | ||
- [ROOT official repository]https://github.com/root-project/root | ||
- [My GitHub profile]https://github.com/GalinBistrev2 |
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