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AWh1t3

PROFILE

Awh1t3

Worked on the DUNE/ndlar_flow repository to enhance detector data quality and calibration workflows over a three-month period. Delivered updates to light geometry configuration and detector channel mapping, improving the accuracy of light detection data processing and reducing the risk of data misalignment. Refined waveform calibration parameters in YAML to support accurate signal processing for multiple detector modules, organizing calibration data by channel and parameter for traceability. Applied configuration management and debugging skills to ensure maintainability and reproducibility, while minimizing production disruption. Used Python and YAML to manage calibration resources, aligning updates with ongoing monitoring and future validation requirements.

Overall Statistics

Feature vs Bugs

75%Features

Repository Contributions

4Total
Bugs
1
Commits
4
Features
3
Lines of code
309
Activity Months3

Your Network

115 people

Shared Repositories

24
alexbooth92Member
cuddandrMember
Yifan ChenMember
Yifan ChenMember
YifanCMember
DeMario RossMember
DeMario RossMember
DeMario RossMember
Alejandro DiazMember

Work History

February 2025

1 Commits • 1 Features

Feb 1, 2025

February 2025: Delivered targeted waveform calibration refinement in DUNE/ndlar_flow. Updated WaveformCalib.yaml to improve data processing accuracy for Mod 1-3; Mod 0 calibration remains an open area. Result: higher-quality data for downstream analyses and more robust monitoring of the light detection system.

December 2024

1 Commits • 1 Features

Dec 1, 2024

December 2024: Delivered targeted waveform calibration update for the July 2x2 run in DUNE/ndlar_flow, enhancing signal processing accuracy and detector calibration for the ND-LAr flow. The change adds July 2x2 calibration values to WaveformCalib.yaml, organized by channel and parameter to improve traceability and downstream processing. This work is anchored by commit e3b88c9dce2b666fafbcbc1dd22f75c09dbae3df, which adds calibration values from the July 2x2 run. No major bugs fixed this month; ongoing monitoring of calibration data quality continues. Overall impact: improved data quality and reliability for calibration workflows, enabling more accurate physics analyses and better detector performance. Technologies/skills demonstrated: YAML-based calibration data management, version control and traceability, dataset organization by channel/parameter, and alignment with ND-LAr flow calibration workflow.

November 2024

2 Commits • 1 Features

Nov 1, 2024

November 2024: Delivered critical accuracy improvements and a key bug fix in the DUNE/ndlar_flow codebase, reinforcing data quality and maintainability for the FSD flow.

Activity

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Quality Metrics

Correctness85.0%
Maintainability85.0%
Architecture80.0%
Performance75.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

YAMLpythonyaml

Technical Skills

Configuration ManagementData CalibrationData ConfigurationDebuggingDetector SimulationGeometry DefinitionGeometry Management

Repositories Contributed To

1 repo

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

DUNE/ndlar_flow

Nov 2024 Feb 2025
3 Months active

Languages Used

pythonyamlYAML

Technical Skills

Data ConfigurationDebuggingDetector SimulationGeometry DefinitionGeometry ManagementConfiguration Management