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Using SDSS imaging to predict galaxy metallicity. Maintained by @jwuphysics @boada
Estimating galaxy gas mass fractions using SDSS imaging
The SDSS Python template and coding standards.
TensorFlow implementations of a Restricted Boltzmann Machine and an unsupervised Deep Belief Network, including unsupervised fine-tuning of the Deep Belief Network.
The Illustris Virtual Observatory is an expanded iteration of the Sunpy module(ptorrey) for creating synthetic SDSS, HST, or JWST images of galaxies from the Illustris simulation. For instructions on how to install/use this program, please go to this address:
Empirical PSFs of GALEX, SDSS, and 2MASS
Using machine learning to predict the mass of quasar supermassive black holes
GaMorNet is a CNN based on AlexNet to classify galaxies morphologically
A tutorial on classification and photometric redshift regression of astronomical sources using supervised machine learning techniques.
This repository contains the code and data for the Astronomical Object Classification Project. The project focuses on classifying celestial objects (stars, galaxies, and quasars) based on their spectral characteristics using data from the Sloan Digital Sky Survey (SDSS).
Repo for a Data analysis project to find Distances, Luminosities, Fluxes, Star Formation Rates, and WISE image processing for galaxies in the MaNGA survey.
Translation and optimisation of SEDMORPH's PawlikMorph IDL code for analysing images of galaxies from SDSS data release 7
This project is a full machine learning pipeline for Star/Galaxy classification using the SDSS dataset. It also contains a detailed report on the development and a DockerFile to easily replicate the results.
Image API implementation for acquiring SDSS image based on location
asteroid detection from Sloan Digital Sky Survey (SDSS) images (ALPHA - unstable under development)
Repository for ASP Undergraduate Thesis
Calculate Anomalously Low Metallicity (ALM) spaxels and galaxies using MaNGA data. Find the relation between HI gas and ALM.
Phosphorpy is python package to mine multiple databases with astronomical data
Classify stars, galaxies, and quasars with SDSS DR16 data. Balanced dataset using resampling techniques improves AdaBoost classifier's performance, enhancing astronomical object classification accuracy.
All code to reproduce results from "Validation of the Bond et al. (2010) SDSS-derived kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 proper motion and radial velocity data"