Fix Galin's post

Author: vgvassilev

_posts/2025-09-25-Supporting-AD-in-CMS-Combine-likelihood-scans.md

@@ -1,144 +1,142 @@
-title: "Results from CERN Summer School 2025: Supporting Automatic 
-Differentiation in CMS Combine profile likelihood scans"
+---
+title: "Results from CERN Summer School 2025: Supporting Automatic Differentiation in CMS Combine profile likelihood scans"
 layout: post
-excerpt: "A CERN Summer Student 2025 project aiming at the support of 
-automatic differentiation (AD) for likelihood scans in the CMS Combine 
-tool to accelerate statistical inference by leveraging RooFit's AD 
-support and LLVM-based gradient generation."
+excerpt: >
+  A CERN Summer Student 2025 project aiming at the support of
+  automatic differentiation (AD) for likelihood scans in 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_results_blog/
 banner_image: /images/blog/banner-cern.jpg
 date: 2025-09-25
-tags: cern cms root combine c++ RooFit automatic-differentiation	
+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 was working on a 
-project that aimed to provide support for Automatic Differentiation 
+ Nuclear and Particle Physics at the University of Sofia "St. Kliment Ohridski."
+As part of the CERN Summer Student Programme 2025, I was working on a
+project that aimed to provide support for Automatic Differentiation
 (AD) into the CMS Combine tool profile likelihood scans.
 
 Mentors: Jonas Rembser, Vassil Vasilev, David Lange
 
 ### **Description of the Project**
 
-This project aims to enhance support for Automatic Differentiation (AD) 
-in likelihood scans within the CMS Combine framework, the primary 
-statistical analysis tool of the CMS experiment at CERN. Combine is 
-built on top of RooFit, which has recently introduced AD to improve 
-minimization techniques. By providing computationally efficient 
-gradients through AD, RooFit achieves substantial performance 
-improvements. In RooFit, Clad converts internal likelihood 
-representations into standalone C++ code, from which gradient 
-routines for AD are generated. This strategy not only speeds up the 
-fitting process but also increases the portability and shareability 
-of likelihood models, making them usable even by those without 
+This project aims to enhance support for Automatic Differentiation (AD)
+in likelihood scans within the CMS Combine framework, the primary
+statistical analysis tool of the CMS experiment at CERN. Combine is
+built on top of RooFit, which has recently introduced AD to improve
+minimization techniques. By providing computationally efficient
+gradients through AD, RooFit achieves substantial performance
+improvements. In RooFit, Clad converts internal likelihood
+representations into standalone C++ code, from which gradient
+routines for AD are generated. This strategy not only speeds up the
+fitting process but also increases the portability and shareability
+of likelihood models, making them usable even by those without
 detailed knowledge of RooFit or Combine internals.
 
 ### **Brief overview of the CMS Combine engine**
-Combine is a statistical analysis framework that compares models of 
-expected observations with real data. It is widely used for tasks such 
-as searching for new particles or processes, setting limits on 
-potential new physics, and measuring physical quantities like cross-sections. 
-Although developed with High Energy Physics (HEP) 
-applications in mind, Combine contains no intrinsic physics assumptions, 
-making it fully general and independent of any specific analysis. 
-This flexibility allows it to be applied across a broad range of 
+Combine is a statistical analysis framework that compares models of
+expected observations with real data. It is widely used for tasks such
+as searching for new particles or processes, setting limits on
+potential new physics, and measuring physical quantities like cross-sections.
+Although developed with High Energy Physics (HEP)
+applications in mind, Combine contains no intrinsic physics assumptions,
+making it fully general and independent of any specific analysis.
+This flexibility allows it to be applied across a broad range of
 statistical problems.
 
 Roughly, Combine performs three main functions:
 
 - Builds a statistical model of expected observations.
 - Runs statistical tests comparing the model with observed data.
-- Provides tools for validating, inspecting, and understanding both the 
+- Provides tools for validating, inspecting, and understanding both the
 model and the results of the statistical tests.
 
 ### **Project goals**
 
 In order for AD to be supported in Combine likelihood scans, a number of goals needed to be achieved:
 
-- Refactoring some of Combine's logic into RooFit, so that Combine can 
-reuse the AD-enabled minimization algorithm already present there. 
-- Integrate gradient computation into likelihood scans, ensuring that 
-derivatives are correctly propagated for efficient and accurate minimization.  
-- Validate correctness and performance, confirming that the AD-based 
-scans produce results consistent with traditional methods while 
+- Refactoring some of Combine's logic into RooFit, so that Combine can
+reuse the AD-enabled minimization algorithm already present there.
+- Integrate gradient computation into likelihood scans, ensuring that
+derivatives are correctly propagated for efficient and accurate minimization.
+- Validate correctness and performance, confirming that the AD-based
+scans produce results consistent with traditional methods while
 offering improved performance.
 
 ## **Overview of Completed Work**
 Over the course of the project, several major tasks were completed to achieve the stated objectives:
 
-- Imported the `RooMultiPdf` class in RooFit from Combine, enabling 
-switching between multiple PDF-s, applying statistical penalties, 
+- Imported the `RooMultiPdf` class in RooFit from Combine, enabling
+switching between multiple PDF-s, applying statistical penalties,
 and supporting code generation for AD.
- 
-- The implementation of the new class was made to be supported by 
-`codegen` in RooFit by adding a new function in `MathFunc.h` and 
+
+- The implementation of the new class was made to be supported by
+`codegen` in RooFit by adding a new function in `MathFunc.h` and
 extending `CodegenImpl.cxx` to generate code for models making use of it.
-  
-- Imported three pieces of code from Combine that handle the 
-minimization procedures within the framework in RooFit's `RooMinimizer.cxx`. 
-The first is a class imported by Jonas Rembser 
-called `FreezeDisconnectedParametersRAII`, which automatically 
-freezes and unfreezes parameters disconnected from the likelihood graph. 
-The second is the function `generateOrthogonalCombinations`, which 
-generates a list of index combinations by initializing a base 
-configuration with all indices set to zero and then varying one category at a time. 
-The third and final piece of code is a function called `reorderCombinations`, 
-which takes the set of indices produced by `generateOrthogonalCombinations` 
-and adjusts each combination by adding the corresponding base values 
-modulo the maximum allowed index, effectively shifting the combinations 
+
+- Imported three pieces of code from Combine that handle the
+minimization procedures within the framework in RooFit's `RooMinimizer.cxx`.
+The first is a class imported by Jonas Rembser
+called `FreezeDisconnectedParametersRAII`, which automatically
+freezes and unfreezes parameters disconnected from the likelihood graph.
+The second is the function `generateOrthogonalCombinations`, which
+generates a list of index combinations by initializing a base
+configuration with all indices set to zero and then varying one category at a time.
+The third and final piece of code is a function called `reorderCombinations`,
+which takes the set of indices produced by `generateOrthogonalCombinations`
+and adjusts each combination by adding the corresponding base values
+modulo the maximum allowed index, effectively shifting the combinations
 relative to the current best indices.
 
-- Using the above-stated functions, the discrete profiling algorithm, 
-which is the main minimization algorithm in Combine, was imported 
+- Using the above-stated functions, the discrete profiling algorithm,
+which is the main minimization algorithm in Combine, was imported
 into `RooMinimizer.cxx`.
-- A [tutorial](https://root.cern/doc/master/rf619__discrete__profiling_8py.html) 
-was created along with a [benchmark](https://github.com/vgvassilev/clad/issues/1521), 
-made by Jonas Rembser, demonstrating discrete profiling with RooMultiPdf objects 
-and evaluating the performance of AD in the likelihood scans.  
+- A [tutorial](https://root.cern/doc/master/rf619__discrete__profiling_8py.html)
+was created along with a [benchmark](https://github.com/vgvassilev/clad/issues/1521),
+made by Jonas Rembser, demonstrating discrete profiling with RooMultiPdf objects
+and evaluating the performance of AD in the likelihood scans.
 
 ## **Results**
-With those objectives accomplished, RooFit now provides AD support for 
-discrete profiling. However, the developed benchmark indicates that AD 
-does not currently improve efficiency, as the gradient code generated by 
-Clad introduces overhead. Further optimization in Clad is needed to achieve 
-the potential performance gains for RooFit likelihood scans. More information 
+With those objectives accomplished, RooFit now provides AD support for
+discrete profiling. However, the developed benchmark indicates that AD
+does not currently improve efficiency, as the gradient code generated by
+Clad introduces overhead. Further optimization in Clad is needed to achieve
+the potential performance gains for RooFit likelihood scans. More information
 regarding the issue can be found at [#1521](https://github.com/vgvassilev/clad/issues/1521).
 
 ## **Conclusions**
-Thanks to this project, RooFit now enables AD support for discrete profiling in Combine, 
-which, after addressing the current overhead in Clad, would allow for 
-significantly faster and more efficient likelihood scans while maintaining 
-accurate optimization of both discrete and continuous parameters. 
+Thanks to this project, RooFit now enables AD support for discrete profiling in Combine,
+which, after addressing the current overhead in Clad, would allow for
+significantly faster and more efficient likelihood scans while maintaining
+accurate optimization of both discrete and continuous parameters.
 
 ## **Future work**
-- Further benchmarking is required to quantify the potential performance 
+- Further benchmarking is required to quantify the potential performance
 gains from automatic differentiation.
-- Additional optimization of Clad is needed to eliminate unnecessary 
+- Additional optimization of Clad is needed to eliminate unnecessary
 overhead in gradient generation.
-- The discrete profiling logic implemented in RooMinimizer should be 
-tested across different models to evaluate the minimizer’s behavior and 
+- The discrete profiling logic implemented in RooMinimizer should be
+tested across different models to evaluate the minimizer’s behavior and
 robustness.
-- Extend doxygen documentation of RooMinimizer to describe treatment of discrete 
+- Extend doxygen documentation of RooMinimizer to describe treatment of discrete
 parameters.
-- Test if the implementation of discrete profiling works also inside CMS Combine , 
-replacing their implementation in `CascadeMinimizer.cxx`. 
+- Test if the implementation of discrete profiling works also inside CMS Combine ,
+replacing their implementation in `CascadeMinimizer.cxx`.
 
 ## **Acknowledgements**
-I would like to express my sincere gratitude to the CERN Summer School 
-for the opportunity to participate in such an inspiring project. 
-I extend special thanks to Jonas Rembser, Vassil Vassilev, and David Lange for 
-their invaluable guidance and for providing continuous learning opportunities throughout this journey. 
-I am also grateful to the ROOT team for welcoming me and supporting me throughout my stay at CERN. 
+I would like to express my sincere gratitude to the CERN Summer School
+for the opportunity to participate in such an inspiring project.
+I extend special thanks to Jonas Rembser, Vassil Vassilev, and David Lange for
+their invaluable guidance and for providing continuous learning opportunities throughout this journey.
+I am also grateful to the ROOT team for welcoming me and supporting me throughout my stay at CERN.
 
 ## **Related Links**
-- [CMS Combine GitHub page](https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/latest/)  
-- [ROOT official repository](https://github.com/root-project/root)  
+- [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)
 - [Presentation](/assets/presentations/CaaS_Weekly_25_09_2025_Galin_Bistrev_AD_in_CMS_Combine.pdf)
-
-
-