Analysis

Sometimes, we wish to do further analysis after we fit. Or before fitting get a understanding of the priors to set, develop a new model, plot the spectrum etc. In redback we have a Analysis class that can be used to do this.

This class provides wrappers around the plot_lightcurve/plot_multiband_lightcurve functions in redback.plotting and provides a plot_spectrum function and some additional diagnostic/supplementary plots that are specific to certain models.

For example, if we wanted to plot a some specific parameters on top of the plot_lightcurve plot, we can do the following:

from redback import analysis

analysis.plot_lightcurve(transient, parameters, model, model_kwargs=None)

Here

  • transient is the transient object.

  • Parameters is a dictionary or pandas dataframe containing the parameters we wish to plot.

  • model is the model we wish to plot. This can be a string or a function.

  • model_kwargs is a dictionary of keyword arguments to pass to the model function.

Spectral Template Matching

The SpectralTemplateMatcher class provides SNID-like spectral classification:

from redback.analysis import SpectralTemplateMatcher

matcher = SpectralTemplateMatcher()
result = matcher.match_spectrum(spectrum, redshift_range=(0, 0.3))
print(f"Type: {result['type']}, z={result['redshift']:.3f}")

For detailed documentation on spectral template matching, see Spectral Template Matching.

Spectral Velocity Fitting

The SpectralVelocityFitter class provides tools for measuring expansion velocities from spectral line profiles. This is particularly useful for analyzing Type Ia supernovae and other transients with P-Cygni profiles.

from redback.analysis import SpectralVelocityFitter

# Create fitter
fitter = SpectralVelocityFitter(wavelength, flux)

# Measure Si II 6355 velocity
v, v_err = fitter.measure_line_velocity(6355)
print(f"Si II velocity: {v:.0f} +/- {v_err:.0f} km/s")

# Measure multiple lines
lines = {'Si II': 6355, 'Ca II': 3934, 'Fe II': 5169}
velocities = fitter.measure_multiple_lines(lines)

# Track velocity evolution
times, vels, errs = SpectralVelocityFitter.photospheric_velocity_evolution(
    wavelength_list, flux_list, times, line_wavelength=6355
)

# Detect high-velocity features
has_hvf, v_hvf, err = fitter.identify_high_velocity_features(6355, v_phot_expected=11000)

For detailed documentation on spectral line models and velocity fitting, see Spectral Line Profile Models.

Please look at the API for the other methods available in the Analysis class.