DeepGlow is a feed-forward neural network trained to emulate BOXFIT simulation data of gamma-ray burst (GRB) afterglows. This package provides an easy interface to generate GRB afterglow spectra and light curves mimicking those generated through BOXFIT with high accuracy. Details are provided in the published paper: doi.org/10.1017/pasa.2023.32.
This repository also contains the code used to generate the training data and to train the neural networks.
Installation is straightforward via pip:
pip install DeepGlow
DeepGlow specifically requires numpy, TensorFlow 2.x.x, and the importlib.resources package. For testing, pytest is needed as well.
To generate light curves or spectra, create an instance of the Emulator class specifying the progenitor environment (either ism or wind):
from DeepGlow import Emulator
model = Emulator(simtype='ism')
The flux function of the Emulator class returns the flux values in mJy. It takes the three arguments params, t_obs and nu_obs corresponding to an array of the GRB afterglow parameters, observing times in seconds and observing frequencies in Hz. Each observing time value in the t_obs array must correspond to an observing frequency value in the nu_obs array. The afterglow parameters need to be specified in the following order in the params array:
-
$z$ : redshift. -
$\log_{10} d_{L,28}$ : luminosity distance (log10 of 10^28 cm). -
$\log_{10} E_\mathrm{K,iso,53}$ : isotropic-equivalent energy (log10 of 10^53 erg). -
$\log_{10} n_\mathrm{ref}$ : circumburst medium density (log10 of cm^-3). -
$\theta_0$ : jet half-opening angle (rad). -
$\theta_\mathrm{obs} / \theta_0$ : off-axis observer angle as a fraction of the jet half-opening angle. -
$p$ : electron spectral index. -
$\log_{10} \bar{\epsilon}_e \equiv \frac{p-2}{p-1} \epsilon_e$ : energy fraction in accelerated electrons (in log10), with factor of$(p - 2) / (p - 1)$ absorbed. -
$\log_{10} \epsilon_B$ : energy fraction in magnetic field (in log10). -
$\log_{10} \xi_N$ : fraction of electrons accelerated (in log10).
For example:
import numpy as np
GRB_params = np.array([0,-1,0,0,0.1,0.1,2.2,-2,-2,0])
observing_times=np.array([1e5,1e6,1e7])
observing_frequencies = np.array([1e9,1e12,1e15])
flux_values = model.flux(params=GRB_params,t_obs=observing_times,nu_obs=observing_frequencies)
print(flux_values)
# [5.75068180e-01, 8.58790301e-01, 5.39014321e-05]
The training data generated with BOXFIT is provided through a Zenodo data package: https://zenodo.org/record/7472542
- DeepGlow paper: https://doi.org/10.1017/pasa.2023.32
- BOXFIT: van Eerten, H., Horst, A. v. d., & MacFadyen, A. 2012, The Astrophysical Journal, 749, 44