add midterm blog post for Soumyadip Niyogi (gsoc26)

_gsocblogs/2026/blog_Midterm_Integration_of_Combine_with_FCCAnalyses_SoumyadipNiyogi.md

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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) |
+
+## 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.
+
+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).
+
+## Progress So Far: The Core Engine
+Over the past month, I have successfully implemented the `fit.py` interface within the `FCCAnalyses` framework. Here is what the tool can currently do:
+
+*   **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.
+
+## The Technical Challenge: ROOT
+In the beginning I ran into a curious technical roadblock. When testing the `-e` direct execution flag on our generated datacards using Combine's `AsymptoticLimits` method, the framework kept throwing a fatal C++ exception: `Caught exception Value 2.2 is outside the default range [0, 1.76]`. 
+
+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!
+
+**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.
+
+## Next Steps
+With the core generation and execution engine validated, the second half of the summer will focus on the physics and usability.
+
+**Full Statistics Validation:** Moving past the Asimov testing phase to run the framework on full-statistics ZH → μμ background and signal shapes.
+
+**Documentation:** Writing the user tutorials and `pytest` suite so the FCC community can actually learn to use the interface.
+
+## Acknowledgements
+A huge thank you to my mentors, Jan and Juraj, for their responsive feedback on Mattermost and for helping me decipher cryptic ROOT exceptions. I am looking forward to bringing this tool to the finish line over the next couple of months.