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              | Original file line number | Diff line number | Diff line change | 
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| --- | ||
| 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. | ||
|  | ||
| ### Project description | ||
|  | ||
| 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. | ||
|  | ||
| ### 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 | ||
|  | ||
| ### Project goals | ||
| The main goals of this project are: | ||
|  | ||
| - Support external users in working with Combine-generated models without needing | ||
| to dive into RooFit or Combine internals. | ||
|  | ||
| - Optimize performance in high-complexity workflows within the Combine framework. | ||
|  | ||
|  | ||
| ### Implementation strategy | ||
|  | ||
| - Refactor Combine to use standard RooFit primitives where possible, and add AD support to custom components. | ||
|  | ||
| - Integrate RooFit's AD- generated gradients into Combines's likelihood | ||
| evaluation and minimization workflows to improve performance and enable external | ||
| gradient use. | ||
|  | ||
|  | ||
| - Developing benchmarks to quantitatively demonstrate the performance improvements AD brings to Combine overflows. | ||
|  | ||
| ##Conclusion | ||
|  | ||
| 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. | ||
|  | ||
| ### 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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