February 2026 performance highlights focused on expanding data analysis capabilities for LLH workflows, strengthening robustness, and improving code quality across MaCh3 and MaCh3Tutorial. The team delivered new profiling-based likelihood generation and visualization features, along with CI-driven improvements to the LLHMap fitting process, enabling more reliable and automated validation of results for faster decision-making.

2026-01 MaCh3 monthly summary for mach3-software/MaCh3. Focused on delivering scalable likelihood mapping capabilities and clearer visualization to support uncertainty quantification and design decisions. Implemented an initial general multi-dimensional likelihood scan in the FitterBase class, with improvements to parameter handling, range initialization, logging, and robustness of the RunLLHMap workflow to enable reliable, scalable analyses. Also enhanced triangle plot visuals to improve readability and presentation for likelihood mapping results.