Core Stingray Functionality

Here we show how many of the core Stingray classes and methods work in practice. We start with basic data constructs for event data and light curve data, and then show how to produce various Fourier products from these data sets.

Working with Event Data

• Contents
• Setup
  • Creating EventList from Photon Arrival Times
• Roundtrip to Astropy-compatible formats
  • Loading and writing EventList objects
• Loading an EventList from an X-ray observation in HEASoft-compatible format
• Roundtrip to pickle objects
• Roundtrip to Astropy-compatible formats
  • Transforming a Lightcurve into an EventList.
  • Simulating EventList from Lightcurve
  • Creating a light curve from an EventList object
  • Simulating Energies
  • Joining EventLists

Working with Lightcurves

• Creating a light curve
  • 1. Array of time stamps and counts
  • 2. Photon Arrival Times
• Properties
  • Error Distributions in stingray.Lightcurve
  • Good Time Intervals
• Operations
  • Addition/Subtraction
  • Negation
  • Indexing
• Methods
  • Concatenation
  • Truncation
  • Re-binning
  • Sorting
  • Plotting
• Sample Data
  • Checking the Light Curve for Irregularities
  • MJDREF and Shifting Times
  • Calculating a baseline
  • Working with GTIs and Splitting Light Curves
  • Analyzing Light Curve Segments
  • Compatibility with Lightkurve
  • Reading/Writing Lightcurves to/from files
• R.m.s. - intensity diagram
  • Setup: simulate a light curve with a variable rms and rate
  • R.m.s. - intensity diagram

Fourier Analysis

Powerspectra

• Power spectrum example
  • 1. Create a light curve
  • 2. Pass the light curve to the Powerspectrum class to create a Powerspectrum object.
• Properties
  • 2. Pass the light curve to the AveragedPowerspectrum class with a specified segment_size.
• Normalizating the power spectrum
• Re-binning a power spectrum in frequency
  • 2. And we can logarithmically/geometrically re-bin a power spectrum
• Power spectra of normal-distributed light curves

Dynamical Power Spectra

• Dynamical Power Spectra (on fake data)
  • Generate a fake lightcurve
• A power spectrum of this lightcurve..
  • It looks like we have at least 2 frequencies.
• Let’s look at the Dynamic Powerspectrum..
  • It is actually only one feature drifiting along time
• Rebin time
  • Let’s trace that drifiting feature.
• Overlaying this traced function with the Dynamical Powerspectrum
• Dynamical Power Spectra (on real data)
• All starts with a lightcurve..
• DynamicPowerspectrum
• Rebin time
• Trace maximun

Cross Spectra

• Cross Spectra
  • 1. Create two light curves
  • 2. Pass both of the light curves to the Crossspectrum class to create a Crossspectrum object.
• Properties
  • 2. Pass both light curves to the AveragedCrossspectrum class with a specified segment_size.
• Properties
• Normalizating the cross spectrum
• Re-binning a cross spectrum in frequency
  • 2. And we can logarithmically/geometrically re-bin a cross spectrum
• Time lags / phase lags
  • 1. Frequency-dependent lags
  • 2. Energy-dependent lags
• Coherence

Cross- and Autocorrelations

• CrossCorrelation
• CrossCorrelation Example
• 1. Create two light curves
• 2. Create a CrossCorrelation Object from two Light curves created above
• 3. Plot Cross Correlation for Different lags
  • Modes of Correlation
  • Another Example
  • Yet another Example with longer Lightcurve
  • AutoCorrelation
• Another Example

Bispectra

• Bispectrum Tutorial
• Plots
• Another Example
• Window Functions for Bispectrum
  • Plot Window
  • Another Window demonstrated

Bayesian Excess Variance

• Baysian Excess Variance (Bexvar)
  • Bexvar: Theoretical background

Multi-taper Periodogram

• Install Stingray in colab
• Multitaper Spectral Estimator Example
• ### Creating a light curve
  • The Multitaper Periodogram
  • Summary of Multitaper Spectral Estimation
• Creating a Multitaper object
  • The results
  • While it seems decent, lets compare with Powerspectrum
  • Attributes of the Multitaper object
  • A look at the values contained in these attributes.
  • A look at the different normalizations
  • Other attributes with the S(f) estimates
• A summary of the jackknife variance estimate
  • A look at jk_var_deg_freedom
  • Linearly re-binning a power spectrum in frequency
  • Poisson distributed lightcurve
  • Comparing Powerspectrum and Multitaper on poisson-distributed lightcurve
• Time series with uneven temporal sampling: Multitaper Lomb-Scargle
  • Testing the Multitaper Lomb-Scargle on a Kepler dataset (used in A. Springford et al. (2020) )
• References

Lomb Scargle Crossspectrum

• Lomb Scargle Cross Spectra
• 1. Create two light curves
  • 2. Pass both of the light curves to the LombScargleCrossspectrum class to create a LombScargleCrossspectrum object.
  • Properties

Lomb Scargle Powerspectrum

• Lomb Scargle Power Spectra
• 1. Create a light curve
  • 2. Pass the light curve to the LombScarglePowerspectrum class to create a LombScarglePowerspectrum object.
  • Attributes