Lomb-Scargle Periodogram

The Lomb-Scargle periodogram is the standard tool for finding periods in unevenly sampled astronomical time series data. It's particularly useful for detecting periodic signals in light curves from space missions like Kepler, K2, and TESS.

Overview

The Lomb-Scargle periodogram extends the classical periodogram to handle unevenly sampled data, which is common in astronomy due to observing constraints, data gaps, and variable cadences.

Basic Usage with Lightkurve

import lightkurve as lk
import numpy as np

# Create a light curve object
lc = lk.LightCurve(time=time, flux=flux, flux_err=error)

# Create periodogram (specify maximum period to search)
pg = lc.to_periodogram(maximum_period=15)  # Search up to 15 days

# Find strongest period
strongest_period = pg.period_at_max_power
max_power = pg.max_power

print(f"Strongest period: {strongest_period:.5f} days")
print(f"Power: {max_power:.5f}")

Plotting Periodograms

import matplotlib.pyplot as plt

pg.plot(view='period')  # View vs period (not frequency)
plt.xlabel('Period [days]')
plt.ylabel('Power')
plt.show()

Important: Use view='period' to see periods directly, not frequencies. The default view='frequency' shows frequency (1/period).

Period Range Selection

Choose appropriate period ranges based on your science case:

• Stellar rotation: 0.1 - 100 days
• Exoplanet transits: 0.5 - 50 days (most common)
• Eclipsing binaries: 0.1 - 100 days
• Stellar pulsations: 0.001 - 1 day

# Search specific period range
pg = lc.to_periodogram(minimum_period=2.0, maximum_period=7.0)

Interpreting Results

Power Significance

Higher power indicates stronger periodic signal, but be cautious: