September 2025 performance instrumentation for DUNE/larnd-sim. Delivered NVTX profiling ranges in simulate_pixels.py to enable end-to-end visibility across critical paths, including configuration loading, HDF5 file loading, electron quenching and drifting, and light information loading. This instrumentation provides the data needed to identify bottlenecks, quantify improvements from future optimizations, and accelerate performance-driven development. Commits enabling this work: 28a8bcd573cdedba833846adc8bf96737c6a9ced; b56354445c3ec8b88a45ff0161fef6d130117bf5.

August 2025 focused on reliability, data integrity, and reproducibility across DUNE/larnd-sim and DUNE/ndlar_flow. Key outcomes include bug fixes in simulation data handling and time-stamping, and the introduction of a notebook-driven workflow to harmonize simulation thresholds with data processing. These efforts improved simulation fidelity, reduced downstream errors, and strengthened alignment between data and MC.

Concise monthly summary for 2025-07 focusing on key accomplishments, major deliverables, and impact for DUNE/larnd-sim.

June 2025 monthly development overview focused on pedestal handling, MC pedestal workflows, and simulation reproducibility across two key DUNE repos (larnd-sim and ndlar_flow). The work enhances data realism, calibration fidelity, and processing reliability, delivering tangible improvements to simulation output quality and ease of MC integration.

May 2025 monthly summary focusing on key business value and technical achievements across DUNE/ndlar_flow and DUNE/larnd-sim. Key items: - Implemented per-thread RNG state management in larnd-sim for thread-local randomness, improving simulation accuracy and reproducibility of particle position generation. - Fixed light detector index mapping in Light Event Generation in ndlar_flow by correcting YAML config-driven z-direction swaps, ensuring proper data processing. Overall impact: higher data quality, more reliable simulations, reduced debugging time. Technologies demonstrated: concurrency/threading, RNG state management, config-driven debugging, data pipeline integrity.

April 2025: No new feature deployments for DUNE/ndlar_flow. Primary focus on stabilizing the light-event processing pipeline by fixing channel mapping after io_group swaps.

March 2025 focused on enhancing data processing fidelity and detector-geometry accuracy in the DUNE ND/LArND simulation stack. Key advancements include network-agnostic disabled channel and chip filtering, alignment with updated CRS layouts for Proto-ND, and per-TPC drift direction handling with corrected tile layouts. Pixel layout updates with versioning, 2x2_old_response adjustments, and renamed layout files, plus end-to-end input generation refresh via get_proto_nd_input.sh. These changes reduce data losses from disabled channels, improve simulation realism, and establish reproducible configuration baselines for releases.

February 2025 monthly summary for DUNE/ndlar_flow: Implemented Disabled Channel Filtering in Raw Event Generation to improve data quality and processing efficiency. The feature loads and stores disabled channel data from a JSON file in the Geometry resource and uses this to construct a mask in RawEventGenerator, ensuring data from disabled channels is not processed. This configuration-driven approach reduces noise and unnecessary computation, and improves reproducibility across runs.

November 2024 monthly summary for DUNE/ndlar_flow focusing on calibration data handling improvements and bug fixes that enhance data quality, reliability, and deployability. Key changes revert unintended reversions, correct unit handling for timing, and introduce configuration-driven calibration parameters and defaults to ensure robust operation across environments.

October 2024: Delivered visualization improvements in DUNE/ndlar_flow, expanding 2D Event Display coverage, removing legend repetition, and correcting saturated-hit colors. These changes enhanced data interpretability and reliability for analysts, enabling broader data inspection with clear visuals. The work emphasizes business value in data visualization reliability and paves the way for future UX and performance enhancements.