matched-filtering
CommunityDetect gravitational waves with matched-filtering.
Data & Analytics#SNR#signal-detection#gravitational-waves#matched-filtering#pycbc#template-waveforms
AuthorKaiserWhoLearns
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Matched filtering is the primary technique for detecting gravitational wave signals in noisy detector data. It correlates known template waveforms with the detector data to find signals with high signal-to-noise ratio (SNR).
Core Features & Use Cases
- Time-domain and frequency-domain waveform generation using PyCBC for template creation.
- Data conditioning, PSD handling, SNR calculation, and edge-crop removal.
- Use Case: Detect binary black hole merger signals in detector data by testing multiple templates and extracting peak SNR values.
Quick Start
Run a complete matched-filtering workflow on a conditioned gravitational-wave data segment using a generated template waveform with PyCBC
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: matched-filtering Download link: https://github.com/KaiserWhoLearns/skillsbench/archive/main.zip#matched-filtering Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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