add midterm blog post for Soumyadip Niyogi (gsoc26)#1945
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Pull request overview
This pull request adds a new GSoC 2026 midterm progress blog post to the HSF website content, describing the current state of the “Integration of CMS Combine with FCCAnalyses” project.
Changes:
- Adds a new midterm blog post markdown file for Soumyadip Niyogi.
- Documents current implementation status (datacard generation + execution flag) and a ROOT/Combine issue encountered during validation.
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| | Name | [Soumyadip Niyogi](https://github.com/captainvogon) | | ||
| | Organisation | [IISER Thiruvananthapuram](https://www.iisertvm.ac.in/), [CERN](https://home.cern/), [HSF](https://hepsoftwarefoundation.org/) | | ||
| | Mentor | [Juraj Smiesko](https://kjvbrt.org/) (CERN), [Jan Eysermans](https://github.com/jeyserma) (MIT) | | ||
| | Project | [Integration of CMS Combine with FCCAnalyses](https://summerofcode.withgoogle.com/programs/2026/projects/FyDjzZ3Y) | |
| Initially, I thought our physics statistics were too low, or that the datacard was structurally broken. After some debugging with my mentors, we discovered the real culprit was ROOT 6.30. | ||
| The newest version of ROOT bundled in the Key4hep nightly builds has deprecated the old "silent boundary clipping" behavior. When running `AsymptoticLimits` on our perfectly smooth Asimov dataset, Combine's internal minimizer (Minuit) would aggressively guess a parameter boundary, step slightly out of bounds to test a value, and instantly trigger this new, strict ROOT exception, crashing the entire job! | ||
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| **The Validation:** To bypass Combine's range-guessing and prove that my Python code was generating valid datacards, we switched the backend to run a `-M FitDiagnostics` test instead. The fit converged perfectly on our fake dataset, yielding `Best fit r: 1.0` with roughly a ~1.25% uncertainty. This proof that the datacards are structurally sound was a massive relief. |
kjvbrt
reviewed
Jul 24, 2026
| ## Introduction | ||
| Hi again! We are at the midpoint of GSoC 2026, and I am excited to share the progress on my project bridging `FCCAnalyses` and `CMS Combine`. Since my introductory post, I have spent my time deep in the Python architecture of `FCCAnalyses`, transforming ROOT histograms from simulated e⁺e⁻ → ZH events into automated statistical models. | ||
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| The primary goal of this first half was to build the core datacard generation engine. I am happy to report that the automated pipeline is now up and running. You can check out the finalized skeleton framework in [my latest Pull Request here](https://github.com/HEP-FCC/FCCAnalyses/pull/520). |
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it's no longer a skeleton :)
kjvbrt
reviewed
Jul 24, 2026
| Over the past month, I have successfully implemented the `fit.py` interface within the `FCCAnalyses` framework. Here is what the tool can currently do: | ||
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| * **Automated Datacard Generation:** The framework reads a user-defined, object-oriented Python configuration and automatically maps the requested signal and background processes to their respective ROOT shape histograms. It then spits out a perfectly formatted text-based `datacard.txt`. | ||
| * **The Execution Flag (`-e`):** To make the workflow completely seamless, I added a backend hook. By simply appending `-e` to the CLI command, the framework passes the newly generated datacard directly into a `subprocess` to execute `Combine` natively, eliminating the need for physicists to manually string together terminal commands. |
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You can maybe mention that the additional parameters for Combine can be provided after -- or in the fit script.
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This pull request adds my midterm progress blog post for the GSoC 2026 project "Integration of CMS Combine with FCCAnalyses".