stellar-classification

Interactive ML web app classifying stars, galaxies, and quasars from SDSS DR17 photometric data using a Random Forest model, deployed with Streamlit.

CECILIA — Classificação estelar e asterossismologia com ML sobre Gaia DR3, LAMOST e APOKASC-3. Projeto Pleiades / LAViD-UFPB.

Advanced SDSS stellar classification — multi-class RF/SVM on photometric bands + redshift. >97% accuracy with colour-magnitude diagrams and per-class confidence output.

Machine learning solution for Kaggle Playground Series S6E6 to classify celestial objects (Galaxy, Star, QSO) with 0.95445 Balanced Accuracy.

Cosmic intelligence research lab — gravitational physics simulations, black hole lensing visualisation, spacetime curvature modelling, and AI-assisted astronomical data analysis and classification tools.

Machine-learning and deep-learning models for reconstructing Gamma-Ray Burst (GRB) light curves during my NAOJ Winter Research Internship (2024–25). Includes LSTM, Bi-LSTM, GRU, Transformer experiments, and classical statistical modeling pipelines.

GRB-research-archive: A public repository for curated gamma-ray burst datasets and associated supernova metadata. Designed for scalability and reproducibility.

Photometric stellar object classifier distinguishing Stars, Galaxies and Quasars from SDSS u/g/r/i/z magnitudes and redshift. Includes PCA feature projection and CMD plots.

ML system for classifying stars and predicting atmospheric parameters from spectroscopic survey data

This repository contains exploratory data analysis of stellar data and use unsupervised and supervised learning to classiify them into stars, quasars and galaxies.

Stellar Classification

Automatic classification of O-, B-, and A-type stars using Gaia DR3 low-resolution spectra and machine learning.

Classification of Stars, Galaxies and Quasars using Sloan Digital Sky Survey DR17 Dataset.

Neural-network classification of SDSS galaxies, quasars and stars using PyTorch.