jetsimpy

Hydrodynamic simulations of relativistic blastwave with tabulated angular energy and Lorentz factor profiles. Efficient Gamma-Ray Burst afterglow modeling.

Code paper: Wang et al. 2024.

Important changes (May 3. 2024)

A small change of the interface. Now the interface should be stable (I hope)! A documentation is under development.

Features

These features are currently supported:

• Tabulated angular energy profile
• Tabulated angular Lorentz factor profile
• ISM / wind / mixed external density profile: $n=n_{\rm ism}+n_{\rm wind}(r/10^{17}{\rm cm})^{-2}$
• Synthetic afterglow light curves
• Apparent superluminal motion
• Sky map and Gaussian equivalent image size

Additionally, you can add your own emissivity model by defining a lambda function in a c++ source file. This might be helpful if you have a more complicated model such as Synchrotron self-absorption or other cool stuffs. After adding your own model, just install the package as usual and refer to this model with its model name from Python side.

These features are not supported yet:

• Reverse shock
• Energy injection

Installation

Use the package manager pip to install jetsimpy.

pip install .

Clean up the installation directory. This help avoid conflicts for future installations.

python setup.py clean

You may install the package and clean up the directory by a single line:

pip install . && python setup.py clean

Quickstart

import numpy as np
import jetsimpy

# parameter dictionary
P = dict(
    Eiso = 1e53,        # Isotropic equivalent energy (erg)
    lf = 600,           # initial Lorentz factor
    theta_c = 0.1,      # half opening angle
    n0 = 1,             # ism number density
    eps_e = 0.1,        # epsilon_e
    eps_b = 0.01,       # epsilon_b
    p = 2.17,           # electron power index
    theta_v = 0.4,      # viewing angle
    d = 474.33,         # luminosity distance (Mpc)
    z = 0.1,            # redshift
)

# time and frequency
tday = np.logspace(-2, 3, 100)
tsecond = tday * 3600 * 24
nu = 1e15

# hydro simulation
jet = jetsimpy.Jet(
    jetsimpy.Gaussian(P["theta_c"], P["Eiso"], lf0=P["lf"]),    # jet profile
    0.0,               # wind number density scale
    P["n0"],           # ism number density scale
)

# flux density [mJy]
flux_density = jet.FluxDensity(
    tsecond,           # [second] observing time span
    nu,                # [Hz]     observing frequency
    P,                 # parameter dictionary
)

More examples are available in the example folder.