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Wenqiang Gu

PROFILE

Wenqiang Gu

Wenqiang Gu developed and maintained advanced simulation and reconstruction configurations for the DUNE experiment, focusing on the dunereco and dunesw repositories. He engineered modular, configurable pipelines for signal processing, image analysis, and detector simulation, leveraging Jsonnet and FHiCL to enable flexible, reproducible workflows. His work included integrating deep neural network ROI processing, refining detector geometry alignment, and introducing dynamic controls for resampling and electronics gain. By separating configuration artifacts and enhancing parameter traceability, Wenqiang improved maintainability and data fidelity. His contributions demonstrated depth in configuration management, data processing, and software architecture, directly supporting robust, scalable physics analysis pipelines.

Overall Statistics

Feature vs Bugs

91%Features

Repository Contributions

14Total
Bugs
1
Commits
14
Features
10
Lines of code
2,629
Activity Months7

Work History

July 2025

1 Commits • 1 Features

Jul 1, 2025

July 2025 Monthly Summary for DUNE/dunereco: Implemented a focused enhancement to simulation configuration by adding a new parameter 'response_plane' to wcls_simchannel_sink in the simchannel jsonnet. This ensures the correct response plane is used during simulations, improving fidelity and reliability of results. Committed as b43e914746c3a87894bd7f0c770828db5d3a7ddb with message 'configure the correct response plane position'.

May 2025

2 Commits • 1 Features

May 1, 2025

May 2025 monthly summary for DUNE/dunereco focusing on Protodunevd Experiment updates. Delivered modular configuration changes and dynamic resampler control to enhance flexibility, maintainability, and data-driven experimentation. No major bugs reported this month; primary work centered on feature delivery and architectural improvement.

April 2025

2 Commits • 1 Features

Apr 1, 2025

April 2025 performance summary for DUNE/dunereco: Delivered DNN ROI processing enhancements and signal tuning to improve data and Monte Carlo signal extraction. Key refactor of the DNN ROI finding and integration of new data handling increased accuracy and processing efficiency. Implemented tuned filter configurations for both data and MC, reducing processing latency while maintaining physics performance. Focused on APA1 ROI targeting with explicit DNN ROI configuration to enable more precise localization. These changes were carried out with commits ce81364e4cb666741fbd6e449eed25ca50564c05 and 72f64a81b938f382f5ca23f322d9e09203c8f196.

February 2025

1 Commits • 1 Features

Feb 1, 2025

February 2025 (DUNE/dunereco): Delivered WireCell imaging capabilities for the Protodune SP detector and added Protodune SP-tailored image processing configurations. This work enables end-to-end imaging-based event reconstruction and establishes pipelines for pre-processing, slicing, tiling, and clustering. Changes are implemented under commit fe7d30aef91c587866c6b51a156b61a04e141c35 and are CI-ready for integration into the main branch.

January 2025

3 Commits • 2 Features

Jan 1, 2025

Concise monthly summary for 2025-01 focused on delivering configurable ProtoDUNE HD simulation/reconstruction setup and enhancing flexibility through electronics gain configuration. The work improves simulation fidelity, reproducibility, and deployment efficiency across two DUNE repositories, aligning with project goals for robust detector analysis.

November 2024

2 Commits • 1 Features

Nov 1, 2024

November 2024 monthly summary: Delivered targeted enhancements in DUNE Dunereco and Dunecore that improve data analysis granularity and simulation geometry fidelity for ProtoDUNE VD. Implemented a Signal-only output configuration with a new JSONNet file and a corresponding FHiCL module to save simulated signal waveforms and digitized ADC values separately, enabling deeper post-processing analytics. Fixed detector geometry alignment by correcting TPC volume positioning relative to CRP boundaries, improving simulation accuracy and reducing systematic uncertainties. These changes demonstrate strong skills in JSONNet/FHiCL tooling, detector geometry modeling, and careful change management, delivering tangible business value through richer data products and more reliable simulations.

October 2024

3 Commits • 3 Features

Oct 1, 2024

October 2024 Monthly Summary: Delivered key enhancements across dunesw and dunereco to advance reconstruction configuration, ROI-based signal analysis, and imaging readiness. Implemented ProtoDUNE-HD Reconstruction Configuration Upgrade to Version 10, integrated DNN-based ROI processing into the signal pipeline, and prepared Wirecell imaging configurations for advanced image analysis. These efforts improve data quality, analysis capabilities, and deployment readiness, directly supporting faster physics insights and scalable imaging workflows.

Activity

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Quality Metrics

Correctness82.2%
Maintainability81.4%
Architecture82.2%
Performance67.2%
AI Usage20.0%

Skills & Technologies

Programming Languages

FCLJsonnetPerlXMLfcljsonnet

Technical Skills

Configuration ManagementData AnalysisData ProcessingData Reconstruction ConfigurationDetector SimulationDetector Simulation ConfigurationGeometry ConfigurationImage ProcessingMachine Learning IntegrationSignal ProcessingSimulation ConfigurationSoftware Architecture

Repositories Contributed To

3 repos

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

DUNE/dunereco

Oct 2024 Jul 2025
7 Months active

Languages Used

Jsonnetjsonnetfcl

Technical Skills

Configuration ManagementData ProcessingMachine Learning IntegrationSignal ProcessingDetector SimulationSimulation Configuration

DUNE/dunesw

Oct 2024 Jan 2025
2 Months active

Languages Used

FCL

Technical Skills

Configuration ManagementDetector SimulationData Reconstruction ConfigurationDetector Simulation Configuration

DUNE/dunecore

Nov 2024 Nov 2024
1 Month active

Languages Used

PerlXML

Technical Skills

Detector SimulationGeometry Configuration

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