
Samuel Fogarty contributed to the DUNE/ndlar_flow and DUNE/larnd-sim repositories by engineering robust data processing and calibration workflows for detector simulation. He overhauled pedestal processing by refactoring code into modular Python classes, enabling multi-file HDF5 input and streamlined JSON output, and automated data generation using Bash scripting. Samuel introduced a lookup-table-based pixel ID mapping to improve geometry handling and consolidated configuration defaults for calibration parameters. In DUNE/larnd-sim, he enhanced simulation reliability by adding explicit out-of-bounds protection in CUDA-accelerated track pixel mapping. His work emphasized maintainability, reproducibility, and throughput, with careful attention to documentation and workflow automation throughout the development process.
July 2025 progress on DUNE/ndlar_flow focused on architectural refactor and automation to scale pedestal processing. Implemented a pedestal processing overhaul into separate classes for histogram generation and pedestal JSON creation, enabling multi-file HDF5 pedestal inputs, with updated configuration and documentation reflecting the new architecture. Added shell-based automation for pedestal data generation for 2x2 and FSD detectors using h5flow, including defined input directories, file lists, outputs, and a workflow YAML. These changes improve throughput, reproducibility, and scalability of the data-processing pipeline.
July 2025 progress on DUNE/ndlar_flow focused on architectural refactor and automation to scale pedestal processing. Implemented a pedestal processing overhaul into separate classes for histogram generation and pedestal JSON creation, enabling multi-file HDF5 pedestal inputs, with updated configuration and documentation reflecting the new architecture. Added shell-based automation for pedestal data generation for 2x2 and FSD detectors using h5flow, including defined input directories, file lists, outputs, and a workflow YAML. These changes improve throughput, reproducibility, and scalability of the data-processing pipeline.
June 2025 focused on delivering core data quality improvements and a streamlined pedestal workflow in DUNE/ndlar_flow. Key features include a Pixel Unique ID generation overhaul that introduces a per-pixel ID mapping using a LUT (later migrated to a dedicated function) and consolidation of ID defaults for pedestal, ADC counts, and gain; a new GeneratePedestals workflow that computes channel pedestals from LArPix data, outputs JSON and HDF5, and is integrated with H5Flow; and targeted calibration fixes addressing prompt hit handling and ADC-to-millivolt calculations. These efforts improve data consistency, processing throughput, and maintainability, enabling more reliable detector calibration, faster onboarding of new data, and clearer documentation.
June 2025 focused on delivering core data quality improvements and a streamlined pedestal workflow in DUNE/ndlar_flow. Key features include a Pixel Unique ID generation overhaul that introduces a per-pixel ID mapping using a LUT (later migrated to a dedicated function) and consolidation of ID defaults for pedestal, ADC counts, and gain; a new GeneratePedestals workflow that computes channel pedestals from LArPix data, outputs JSON and HDF5, and is integrated with H5Flow; and targeted calibration fixes addressing prompt hit handling and ADC-to-millivolt calculations. These efforts improve data consistency, processing throughput, and maintainability, enabling more reliable detector calibration, faster onboarding of new data, and clearer documentation.
April 2025 monthly summary for DUNE/larnd-sim: Implemented an important robustness enhancement by adding explicit out-of-bounds protection in the track pixel mapping code. The boundary checks guard get_track_pixel_map and get_track_pixel_map2 against indexing errors when calculated indices exceed the unique_pix array, significantly reducing crash risk and data corruption in simulations.
April 2025 monthly summary for DUNE/larnd-sim: Implemented an important robustness enhancement by adding explicit out-of-bounds protection in the track pixel mapping code. The boundary checks guard get_track_pixel_map and get_track_pixel_map2 against indexing errors when calculated indices exceed the unique_pix array, significantly reducing crash risk and data corruption in simulations.

Overview of all repositories you've contributed to across your timeline