Concise monthly summary for 2025-03 focusing on delivering core features and bug fixes across two repositories (cta-lstchain and ctapipe) to improve data quality, reliability, and workflow efficiency in analysis pipelines. Key outcomes include corrected alpha correction and uncertainty estimation, enhanced theta-squared table normalization, and scheduling data access for TableLoader with naming alignment. These changes reduce plot misinterpretations, provide more flexible background estimation, and improve end-to-end run reliability and maintainability across projects.

December 2024 monthly summary for cta-observatory/cta-lstchain. Implemented Field-of-View offset bin handling and IRF generation improvements for both point-like and diffuse MC, consolidating changes to standardize FoV binning, align IRF generation workflows, validate FoV bin ranges, refine event filtering to use only in-bin events for energy-dependent cuts, and enhance logging for traceability. These changes improve IRF file accuracy and robustness of downstream analyses. Major bug fix: resolved issue with diffuse MC and energy-dependent theta cut (Fix #1322). Additional safeguards include a warning when FoV binning exceeds the simulated FoV, restricting gammas used for energy-dependent cuts to those within the FoV bin, updating the RAD_MAX HDU shape to match Gammapy expectations, and logging the exact offset binning precision. Overall impact: higher fidelity IRFs, improved reproducibility, and stronger alignment with standard analysis workflows. Technologies/skills demonstrated: Python-based IRF generation, MC/validation workflows, logging and traceability enhancements, data quality checks, and maintainable code organization.