| name | exoplanet-workflows |
| description | General workflows and best practices for exoplanet detection and characterization from light curve data. Use when planning an exoplanet analysis pipeline, understanding when to use different methods, or troubleshooting detection issues. |
Exoplanet Detection Workflows
This skill provides general guidance on exoplanet detection workflows, helping you choose the right approach for your data and goals.
Overview
Exoplanet detection from light curves typically involves:
- Data loading and quality control
- Preprocessing to remove instrumental and stellar noise
- Period search using appropriate algorithms
- Signal validation and characterization
- Parameter estimation
Pipeline Design Principles
Key Stages
- Data Loading: Understand your data format, columns, time system
- Quality Control: Filter bad data points using quality flags
- Preprocessing: Remove noise while preserving planetary signals
- Period Search: Choose appropriate algorithm for signal type
- Validation: Verify candidate is real, not artifact
- Refinement: Improve period precision if candidate is strong
Critical Decisions
What to preprocess?
- Remove outliers? Yes, but not too aggressively
- Remove trends? Yes, stellar rotation masks transits
- How much? Balance noise removal vs. signal preservation
Which period search algorithm?
- TLS: Best for transit-shaped signals (box-like dips)
- Lomb-Scargle: Good for any periodic signal, fast exploration
- BLS: Alternative to TLS, built into Astropy
