nnarenraju/sage
Mitigating ML bias in gravitational-wave detection pipelines
This project helps astrophysicists and gravitational-wave scientists improve the detection of gravitational waves from noisy detector data. It takes raw or processed gravitational-wave detector data as input and provides more accurate identifications of real gravitational-wave signals, particularly binary black hole mergers. It's designed for researchers working with machine learning pipelines for astrophysical signal detection.
Use this if you are a gravitational-wave astrophysicist struggling with machine learning models that miss signals or produce too many false alarms due to inherent biases.
Not ideal if your primary goal is matched-filtering or if you are not working with gravitational-wave data.
Stars
9
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
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