Afterglow modeling
The GRB afterglow observables can be calculated by calling the methods of the jet object jetsimpy.Jet.
Light curve and spectrum
- .FluxDensity(t, nu, P, model='sync', rtol=0.001, max_iter=100, force_return=True)
Calculate the flux density.
- Parameters:
t (np.array/float) – time series (s)
nu (np.array/float) – frequency series (Hz)
P (dict) – parameter dictionary
model (str) – radiation model
rtol (float) – relative tolerance
max_iter (int) – adaptive integration maximum iteration number.
force_return (int) – return the result without throwing an error even if the adaptive integration doesn’t achieve the desired accuracy after the maximum iteration number.
- Returns:
flux density (mJy)
If t is a 1D numpy.array and nu as a scalar, a light curve is generated. If t is a scalar and nu as a 1D numpy.array, a spectrum is generated. t and nu can also be 1D numpy.array of the same length, which is useful in data fitting where different data points have different frequency.
The parameter dictionary P must be compatible with the radiation model (to be explained in the next section). For the default model model=”sync” the required keyword parameters are eps_e (\(\epsilon_{\rm e}\)), eps_b (\(\epsilon_{\rm B}\)), p, theta_v (\(\theta_{\rm obs}\)), d (luminosity distance), and z (redshift).
Frequency integrated flux
- .Flux(t, nu1, nu2, P, model='sync', rtol=0.001, max_iter=100, force_return=True)
Calculate the frequency integrated flux.
- Parameters:
nu1 (np.array/float) – frequency integration lower limit (Hz)
nu2 (np.array/float) – frequency integration upper limit (Hz)
- Returns:
flux (erg/s/cm^2)
Apparent superluminal motion
The flux centroid offset can calculated by the following method
- .Offset(t, nu, P, model='sync', rtol=0.001, max_iter=100, force_return=True)
Calculate the flux centroid offset.
- Returns:
flux centroid offset (MAS)
Image size
- .SizeX(t, nu, P, model='sync', rtol=0.001, max_iter=100, force_return=True)
Calculate the Gaussian equivalent 1-sigma image size along the jet axis.
- Returns:
x direction image size (MAS)
- .SizeY(t, nu, P, model='sync', rtol=0.001, max_iter=100, force_return=True)
Calculate the Gaussian equivalent 1-sigma image size perpendicular to the jet axis.
- Returns:
y direction image size (MAS)
Sky map
- .IntensityOfPixel(t, nu, x_offset, y_offset, P, model='sync')
The intensity of a “pixel” in the image.