Light Curve Preprocessing

Preprocessing is essential before period analysis. Raw light curves often contain outliers, long-term trends, and instrumental effects that can mask or create false periodic signals.

Overview

Common preprocessing steps:

1. Remove outliers
2. Remove long-term trends
3. Handle data quality flags
4. Remove stellar variability (optional)

Outlier Removal

Using Lightkurve

import lightkurve as lk

# Remove outliers using sigma clipping
lc_clean, mask = lc.remove_outliers(sigma=3, return_mask=True)
outliers = lc[mask]  # Points that were removed

# Common sigma values:
# sigma=3: Standard (removes ~0.3% of data)
# sigma=5: Conservative (removes fewer points)
# sigma=2: Aggressive (removes more points)

Manual Outlier Removal

import numpy as np

# Calculate median and standard deviation
median = np.median(flux)
std = np.std(flux)

# Remove points beyond 3 sigma
good = np.abs(flux - median) < 3 * std
time_clean = time[good]
flux_clean = flux[good]
error_clean = error[good]

Removing Long-Term Trends

Flattening with Lightkurve

# Flatten to remove low-frequency variability
# window_length: number of cadences to use for smoothing
lc_flat = lc_clean.flatten(window_length=500)

# Common window lengths:
# 100-200: Remove short-term trends
# 300-500: Remove medium-term trends (typical for TESS)
# 500-1000: Remove long-term trends

The flatten() method uses a Savitzky-Golay filter to remove trends while preserving transit signals.

Iterative Sine Fitting

For removing high-frequency stellar variability (rotation, pulsation):

def sine_fitting(lc):
    """Remove dominant periodic signal by fitting sine wave."""
    pg = lc.to_periodogram()
    model = pg.model(time=lc.time, frequency=pg.frequency_at_max_power)
    lc_new = lc.copy()
    lc_new.flux = lc_new.flux / model.flux
    return lc_new, model

# Iterate multiple times to remove multiple periodic components
lc_processed = lc_clean.copy()
for i in range(50):  # Number of iterations
    lc_processed, model = sine_fitting(lc_processed)

Warning: This removes periodic signals, so use carefully if you're searching for periodic transits.

Handling Data Quality Flags

IMPORTANT: Quality flag conventions vary by data source!

Standard TESS format

# For standard TESS files (flag=0 is GOOD):
good = flag == 0
time_clean = time[good]
flux_clean = flux[good]
error_clean = error[good]

Alternative formats

# For some exported files (flag=0 is BAD):
good = flag != 0
time_clean = time[good]
flux_clean = flux[good]
error_clean = error[good]

Always verify your data format! Check which approach gives cleaner results.

Preprocessing Pipeline Considerations

When building a preprocessing pipeline for exoplanet detection:

Key Steps (Order Matters!)

1. Quality filtering: Apply data quality flags first
2. Outlier removal: Remove bad data points (flares, cosmic rays)
3. Trend removal: Remove long-term variations (stellar rotation, instrumental drift)
4. Optional second pass: Additional outlier removal after detrending

Important Principles

• Always include flux_err: Critical for proper weighting in period search algorithms
• Preserve transit shapes: Use methods like flatten() that preserve short-duration dips
• Don't over-process: Too aggressive preprocessing can remove real signals
• Verify visually: Plot each step to ensure quality

Parameter Selection

• Outlier removal sigma: Lower sigma (2-3) is aggressive, higher (5-7) is conservative
• Flattening window: Should be longer than transit duration but shorter than stellar rotation period
• When to do two passes: Remove obvious outliers before detrending, then remove residual outliers after

Preprocessing for Exoplanet Detection

For transit detection, be careful not to remove the transit signal:

1. Remove outliers first: Use sigma=3 or sigma=5
2. Flatten trends: Use window_length appropriate for your data
3. Don't over-process: Too much smoothing can remove shallow transits

Visualizing Results

Always plot your light curve to verify preprocessing quality:

import matplotlib.pyplot as plt

# Use .plot() method on LightCurve objects
lc.plot()
plt.show()

Best practice: Plot before and after each major step to ensure you're improving data quality, not removing real signals.

Dependencies

pip install lightkurve numpy matplotlib

References

• Lightkurve Preprocessing Tutorials
• Removing Instrumental Noise

Best Practices

1. Always check quality flags first: Remove bad data before processing
2. Remove outliers before flattening: Outliers can affect trend removal
3. Choose appropriate window length: Too short = doesn't remove trends, too long = removes transits
4. Visualize each step: Make sure preprocessing improves the data
5. Don't over-process: More preprocessing isn't always better