exoplanet-workflows

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# 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:
1. Data loading and quality control
2. Preprocessing to remove instrumental and stellar noise
3. Period search using appropriate algorithms
4. Signal validation and characterization
5. Parameter estimation

## Pipeline Design Principles

### Key Stages

1. **Data Loading**: Understand your data format, columns, time system
2. **Quality Control**: Filter bad data points using quality flags
3. **Preprocessing**: Remove noise while preserving planetary signals
4. **Period Search**: Choose appropriate algorithm for signal type
5. **Validation**: Verify candidate is real, not artifact
6. **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

**What period range to search?**
- Consider target star type and expected planet types
- Hot Jupiters: short periods (0.5-10 days)
- Habitable zone: longer periods (depends on star)
- Balance: wider range = more complete, but slower

**When to refine?**
- After finding promising candidate
- Narrow search around candidate period
- Improves precision for final measurement

## Choosing the Right Method

### Transit Least Squares (TLS)
**Use when:**
- Searching for transiting exoplanets
- Signal has transit-like shape (box-shaped dips)
- You have flux uncertainties

**Advantages:**
- Most sensitive for transits
- Handles grazing transits
- Provides transit parameters

**Disadvantages:**
- Slower than Lomb-Scargle
- Only detects transits (not RV planets, eclipsing binaries with non-box shapes)

### Lomb-Scargle Periodogram
**Use when:**
- Exploring data for any periodic signal
- Detecting stellar rotation
- Finding pulsation periods
- Quick period search

**Advantages:**
- Fast
- Works for any periodic signal
- Good for initial exploration

**Disadvantages:**
- Less sensitive to shallow transits
- May confuse harmonics with true period

### Box Least Squares (BLS)
**Use when:**
- Alternative to TLS for transits
- Available in astropy

**Note**: TLS generally performs better than BLS for exoplanet detection.

## Signal Validation

### Strong Candidate (TLS)
- **SDE > 9**: Very strong candidate
- **SDE > 6**: Strong candidate
- **SNR > 7**: Reliable signal

### Warning Signs
- **Low SDE (<6)**: Weak signal, may be false positive
- **Period exactly half/double expected**: Check for aliasing
- **High odd-even mismatch**: May not be planetary transit

### How to Validate

- **Signal strength metrics**: Check SDE, SNR against thresholds
- **Visual inspection**: Phase-fold data at candidate period
- **Odd-even consistency**: Do odd and even transits have same depth?
- **Multiple transits**: More transits = more confidence

## Multi-Planet Systems

Some systems have multiple transiting planets. Strategy:

1. Find first candidate
2. Mask out first planet's transits
3. Search remaining data for additional periods
4. Repeat until no more significant signals

See Transit Least Squares documentation for `transit_mask` function.

## Common Issues and Solutions

From benchflow-ai/skillsbench, Apache-2.0.

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