April 2025 summary: Resolved a build-time regression in the SBNDCode gallery analysis by ensuring the sbnanaobj dependency is correctly resolved during the gallery example build. This fix eliminates a gallery build failure, improving CI reliability and contributor onboarding for SBNSoftware/sbndcode. The change was implemented and validated in the commit set, notably 8ad3ee6b752b7c95c48096188a37d8424b2c5229 (Fixed gallery example build).

March 2025 Monthly Summary for SBND/SBNCode/ICARUS projects focused on delivering LArSoft v10 aligned capabilities, build stability, and clearer dependency structure across SBND and ICARUS codebases. The work emphasizes business value through improved maintainability, downstream readiness, and more robust geometry handling.

February 2025 — SBNSoftware/icaruscode: Delivered three focused improvements to configuration management, code readability, and dependency stability. Key outcomes include eliminating duplicate configuration and CRT geometry, clarifying code semantics, and upgrading the SBNcode dependency to incorporate bug fixes. These changes reduce configuration risk, simplify future maintenance, and improve build reliability.

December 2024 monthly summary for SBNSoftware/sbncode: Delivered a unified GENIE beam configuration template for BNB and NuMI, enabling standardized beam definitions across SBN experiments. Implemented shared configuration files that define beam time structures and parameters (intensity, bucket timing, Gaussian width) for both beams, facilitating consistent and scalable neutrino interaction simulations. This centralizes configuration management and accelerates study pipelines across experiments. No major bugs fixed this month; priorities focused on feature delivery, standardization, and cross-experiment collaboration. Technologies/skills demonstrated include GENIE configuration management, version-controlled template development, cross-repo collaboration, and template-driven configuration for reproducible simulations. Overall impact: improved reproducibility, faster onboarding for new analyses, and reduced setup time for neutrino simulations, delivering tangible business value through standardized, scalable simulation configurations.