light-curve-preprocessing

name	light-curve-preprocessing
description	Preprocessing and cleaning techniques for astronomical light curves. Use when preparing light curve data for period analysis, including outlier removal, trend removal, flattening, and handling data quality flags. Works with lightkurve and general time series data.

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