Multi-purpose lens modeling software package

Python code for calculating non-parametric morphological diagnostics of galaxy images.

Python wrapper for astronomical image-fitting code Imfit (https://github.com/perwin/imfit)

Imfit: an open-source astronomical image-fitting program.

ML framework to estimate Bayesian posteriors of galaxy morphological parameters

Unsupervised galaxy masking to remove background source detections

Official repository of code for "Galaxy Morphology Classification using EfficientNet Architectures"

Repo for my thesis research development at UC Riverside: "Classifying Galaxy Morphologies Using Bayesian Neural Networks for LSST"

Repository of my Bachelor Thesis in Physics about Machine Learning Models For Galaxy Morphology

This repository contains the code Profiler, a Python program for decomposing galaxy light profiles in 1D

Data files, code, and Jupyter notebooks for paper on frequency of bars in spiral galaxies, using S4G data

Galaxy morphology classification with Convolutional Neural Networks

A python based machine learning toolkit for recovering the kinematic disorder of galaxies based on their velocity maps as well as returning an inclination-dependent position angle prediction for further kinematic modelling.

Files and Jupyter notebooks for paper on sizes of bars in spiral galaxies, using S4G data

Using Deep Learning tools in Galaxy Morphology as part of Inter-University Center for Astronomy and Astrophysics (IUCAA)

Using DL (CNNs) and ML (Tree-based methods) on image pixels and other extracted features (eg: FFT) for classification of merger galaxies.

The Paper for Galaxy Morphology Classification using EfficientNets

Website do grupo de pesquisa: IAG Machine Learning - Sistemas inteligentes para descoberta em astronomia. Instituto de Astronomia, Geofísica e Ciências Atmosféricas da Universidade de São Paulo - IAG/USP. Para acessar o código fonte do website, acesse: https://github.com/iagml/website. Este repositório possui apenas código gerado.

Merger galaxies classification using deep learning