jmchen5821's Stars
giacomov/pyggop
Generates templates for gamma-gamma opacity. See the paper "Opacity buildup in impulsive relativistic sources", Granot et al. 2008
giacomov/bbbd
Bayesian Blocks Burst Detection for time series / light curves
TRASAL/frbpoppy
Fast Radio Burst Population Synthesis in Python
FRBs/PreFRBLE
PrEFRBLE: Probability Estimates for Fast Radio Burst to obtain model Likelihood Estimates
dsvinkin/b_blocks
A simple Bayesian block decomposition utility
grburgess/width_calculator
Tools for calculating the 'width' of GRB spectra based off of Axelsson et al. 2015
giacomov/gtburst
GUI for analysis of GRBs and SFs with the Fermi Gamma-Ray Space Telescope
lazzati-astro/MCRaT
Monte Carlo Radiation Transfer Through an Astrophysical Jet
Memcys/Fermi-GRB-Analysis
Scripts for certain Fermi GRB analysis
liuwei840305/notebook
学思笔记
razoumov/radiativeTransfer
astrophysical transfer of diffuse and point-source radiation
RuiningZHAO/Radiative-Transfer-Model
Modeling radiative transfer process around a star
psheehan/pdspy
pdspy: MCMC for Monte Carlo Dust Radiative Transfer Modeling
hyperion-rt/hyperion
Hyperion Radiative Transfer Code
JamesPaynter/PyGRB
Gamma-ray burst analysis library
Memcys/DIY-Python
Learn and share Python for scientific programming
spac3walk3r/Bayesian-hierarchical-modeling-GRBs
NYU-CAL/JetFit
Gamma-ray Burst Afterglow Light Curve Fitting Tool
Husky22/GBMGridSimulator
A Python module using threeML and gbm_drm_gen to simulate and analyse grids of GRB's
JPalmerio/GRB_population_model
This repository is to have versioning of my GRB population code
YWangScience/MCCC
Markov Chain Monte Carlo Correlation Coefficient
YWangScience/AstroNeuron
Machine Learning Applied to Astrophysical Data Analysis
zblz/naima
Derivation of non-thermal particle distributions through MCMC spectral fitting
rsnemmen/Fermi-LAT-tutorial
Tutorial for Fermi LAT analysis hands on session: CTA School 2017, Sao Paulo
amacaluso/Machine-Learning-for-particle-discrimination
This project contains the code to perform a task of Particle identification (PID) in Astrophysics, comparing Deep Learning and Classical Machine Learning approaches. Data are provided by Agile team (http://agile.rm.iasf.cnr.it/) and the goal of the analysis is to provide a Statistical model which is able to distinguish gamma-ray photon for background particles.