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#

Issue: No significant detection (low SDE)#

Solutions:

• Check preprocessing - may be removing signal
• Try less aggressive outlier removal
• Check for data gaps during transits
• Signal may be too shallow for detection

Issue: Period is 2x or 0.5x expected#

Causes:

• Period aliasing from data gaps
• Missing alternate transits

Solutions:

• Check both periods manually
• Look at phase-folded light curves
• Check if one shows odd-even mismatch

Issue: flux_err required error#

Solution: TLS requires flux uncertainties as the third argument - they're not optional!

Issue: Results vary with preprocessing#

Diagnosis:

• Compare results with different preprocessing
• Plot each preprocessing step
• Ensure you're not over-smoothing

Expected Transit Depths#

For context:

• Hot Jupiters: 0.01-0.03 (1-3% dip)
• Super-Earths: 0.001-0.003 (0.1-0.3% dip)
• Earth-sized: 0.0001-0.001 (0.01-0.1% dip)

Detection difficulty increases dramatically for smaller planets.

Period Range Guidelines#

Based on target characteristics:

• Hot Jupiters: 0.5-10 days

• Warm planets: 10-100 days

• Habitable zone:

  • Sun-like star: 200-400 days
  • M-dwarf: 10-50 days

Adjust search ranges based on mission duration and expected planet types.

Best Practices#

1. Always include flux uncertainties - critical for proper weighting
2. Visualize each preprocessing step - ensure you're improving data quality
3. Check quality flags - verify convention (flag=0 may mean good OR bad)
4. Use appropriate sigma - 3 for initial outliers, 5 after flattening
5. Refine promising candidates - narrow period search for precision
6. Validate detections - check SDE, SNR, phase-folded plots
7. Consider data gaps - may cause period aliasing
8. Document your workflow - reproducibility is key

References#

Official Documentation#

• Lightkurve Tutorials
• TLS GitHub
• TLS Tutorials

Key Papers#

• Hippke & Heller (2019) - Transit Least Squares paper
• Kovács et al. (2002) - BLS algorithm

Lightkurve Tutorial Sections#

• Section 3.1: Identifying transiting exoplanet signals
• Section 2.3: Removing instrumental noise
• Section 3.2: Creating periodograms

Dependencies#

bash

pip install lightkurve transitleastsquares numpy matplotlib scipy