In Apr 2025, delivered end-to-end data quality and configuration improvements for uhh-cms/cmsdb, focused on Drell-Yan analytics readiness and maintainable data pipelines. Achievements include calibrated cross-sections, enriched LO DY datasets, and a streamlined dataset configuration that reduces ambiguity in analysis inputs. The work enhances analysis reliability, reproducibility, and onboarding efficiency for data analysts and physics teams.

March 2025 performance snapshot for uhh-cms/cmsdb focused on expanding theoretical precision and simulation coverage for the pre-EE run3 campaign through NNLO Drell-Yan (DY) capabilities. Implemented new NNLO DY datasets with configurable lepton-type and mass-range options, and extended the framework to calculate NNLO cross-sections for DY processes, including extended dilepton and neutrino-pair final states across m_ll. These changes enhance simulation fidelity, improve predictive power for upcoming analyses, and lay the groundwork for broader channel coverage in future sprints.

Performance-focused month for 2024-12 on columnflow/columnflow. Delivered a robust seed generation enforcement by decoupling create_seed and routing all seed creations through a new custom calling mechanism, addressing a bypass bug and adding a vectorized create_seed_vec for higher throughput. This improved reliability, traceability, and seed-generation performance, setting the groundwork for scalable seed pipelines.

2024-11 Monthly Summary for columnflow/columnflow: Focused on boosting reproducibility and stability in the RNG-based jet smearing workflow. Delivered a deterministic seeds feature enabling reproducible smearing across runs when toggled by use_deterministic_seeds. Added deterministic_normal function to derive unique random numbers from event statistics. This change improves test reproducibility, enables reliable cross-run comparisons for physics analyses, and supports more robust CI validations. No major bug fixes were completed this month; work centered on feature delivery and code quality improvements. Technologies demonstrated include flag-based configuration, deterministic RNG design, and event-statistics-driven seeding.