astroNN 1.1.0

Deep Learning for Astronomers with Tensorflow

Getting Started

astroNN is a python package to do various kinds of neural networks with targeted application in astronomy by using Keras API as model and training prototyping, but at the same time take advantage of Tensorflow’s flexibility.

For non-astronomy applications, astroNN contains custom loss functions and layers which are compatible with Tensorflow. The custom loss functions mostly designed to deal with incomplete labels. astroNN contains demo for implementing Bayesian Neural Net with Dropout Variational Inference in which you can get reasonable uncertainty estimation and other neural nets.

For astronomy applications, astroNN contains some tools to deal with APOGEE, Gaia and LAMOST data. astroNN is mainly designed to apply neural nets on APOGEE spectra analysis and predicting luminosity from spectra using data from Gaia parallax with reasonable uncertainty from Bayesian Neural Net. Generally, astroNN can handle 2D and 2D colored images too. Currently astroNN is a python package being developed by the main author to facilitate his research project on deep learning application in stellar and galactic astronomy using SDSS APOGEE, Gaia and LAMOST data.

For learning purpose, astroNN includes a deep learning toy dataset for astronomer - Galaxy10 Dataset.

astroNN Documentation

Quick Start guide

Uncertainty Analysis of Neural Nets with Variational Methods

Acknowledging astroNN

Please cite the following paper that describes astroNN if astroNN is used in your research as well as linking it to https://github.com/henrysky/astroNN

Deep learning of multi-element abundances from high-resolution spectroscopic data [arXiv:1808.04428][ADS]

Authors

• Henry Leung - Initial work and developer - henrysky

  Astronomy Student, University of Toronto

  Contact Henry: henrysky.leung [at] utoronto.ca
• Jo Bovy - Project Supervisor - jobovy

  Astronomy Professor, University of Toronto

License

This project is licensed under the MIT License - see the LICENSE file for details