
Filippo Mei developed foundational data structures and processing utilities for the GRAIN detector within the DUNE/sandreco repository, focusing on robust modeling of both sparse and dense image data, as well as representations for tracklets and clusters. Using C++ and CMake, he implemented modular utilities to extract and count photons and charge from raw SIPM sensor data, establishing a core framework for end-to-end data processing. This work addressed the need for scalable, reliable analysis pipelines by enabling structured signal extraction and future quality metrics. Over the month, Filippo concentrated on building extensible, reusable components rather than bug fixes, ensuring long-term maintainability.

April 2025 – DUNE/sandreco Summary of work: Delivered foundational capabilities for the GRAIN detector by introducing data structures and processing utilities, establishing the core models needed to represent sparse and dense image data, tracklets, and clusters. Added utilities to count photons and charge from raw SIPM data, laying groundwork for end-to-end GRAIN data processing and analysis. Impact and value: This work creates the structural backbone for future signal extraction, quality metrics, and detector performance insights, enabling faster, more reliable analysis of GRAIN outputs and enabling downstream pipelines to scale with data volume. Major bugs fixed: None reported this month; focus was on building the foundation and preparing the pipeline for future work. Technologies/skills demonstrated: Data modeling for image data (sparse and dense), representation of tracklets and clusters, utility development for sensor data extraction (photon and charge counting), and modular, reusable design geared toward pipeline integration.
April 2025 – DUNE/sandreco Summary of work: Delivered foundational capabilities for the GRAIN detector by introducing data structures and processing utilities, establishing the core models needed to represent sparse and dense image data, tracklets, and clusters. Added utilities to count photons and charge from raw SIPM data, laying groundwork for end-to-end GRAIN data processing and analysis. Impact and value: This work creates the structural backbone for future signal extraction, quality metrics, and detector performance insights, enabling faster, more reliable analysis of GRAIN outputs and enabling downstream pipelines to scale with data volume. Major bugs fixed: None reported this month; focus was on building the foundation and preparing the pipeline for future work. Technologies/skills demonstrated: Data modeling for image data (sparse and dense), representation of tracklets and clusters, utility development for sensor data extraction (photon and charge counting), and modular, reusable design geared toward pipeline integration.
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