EhsanGharibNezhad/TelescopeML
Deep Convolutional Neural Networks and Machine Learning Models for Analyzing Stellar and Exoplanetary Telescope Spectra
This tool helps astrophysicists and planetary scientists analyze telescope spectra from exoplanet and brown dwarf atmospheres. You input spectral data, and it processes it using machine learning to predict atmospheric parameters, like composition or temperature. It also provides statistical analysis and visualizations of the data and predictions.
No commits in the last 6 months. Available on PyPI.
Use this if you need to extract atmospheric parameters from exoplanet or brown dwarf spectra using deep learning models and require tools for data preparation, model training, prediction, and statistical validation.
Not ideal if you are not working with stellar or exoplanetary telescope spectra, or if you prefer manual spectroscopic analysis over machine learning approaches.
Stars
14
Forks
18
Language
Python
License
GPL-3.0
Category
Last pushed
Oct 23, 2024
Monthly downloads
39
Commits (30d)
0
Dependencies
13
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/EhsanGharibNezhad/TelescopeML"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
afnx/lifefinder
Exoplanet Life Probability Calculator
nasa/ExoMiner
Automating the vetting and validation of planet candidates from photometry survey missions -...
philippbaumeister/ExoMDN
Rapid characterization of exoplanet interiors with Mixture Density Networks
Pr0-C0der/Exoplanet-Detection-using-CNN
The project aims to leverage machine learning techniques to analyse the flux data and accurately...
ghanashyamrpv/tess-planet-classification
Multiclass ML pipeline for classifying NASA TESS exoplanet candidates from catalog features