Astrophysical and Cosmological Applications of Machine Learnings
================================================================

-  This is Jun, 2019, and an ADS search of “machine learning” in the
   abstract of referred astronomy journals result in **711** papers, so
   it is almost too late to try to “read everything”…but at least we can
   try:

-  And, again, this list only reflects one person’s taste.

--------------

Galaxy Morphology
-----------------

-  `Morpheus: A Deep Learning Framework For Pixel-Level Analysis of
   Astronomical Image
   Data <https://morpheus-project.github.io/morpheus/>`__

   -  By Ryan Hausen & Brant Robertson. Based on the `work
      here <https://arxiv.org/pdf/1906.11248.pdf>`__. `Source code is on
      Github <https://github.com/morpheus-project/morpheus>`__.
   -  **Morpheus** is a neural network model used to generate
      pixel-level morphological classifications for astronomical
      sources. This model can be used to generate segmentation maps or
      to inform other photometric measurements with granular
      morphological information. Built on **Tensorflow**.
   -  `Online document is
      here <https://morpheus-astro.readthedocs.io/en/latest/>`__; and
      `this
      notebook <https://github.com/morpheus-project/morpheus/blob/master/examples/example_array.ipynb>`__
      is a good place to get started.

-  `Rotation-invariant convolutional neural networks for galaxy
   morphology
   prediction <https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1441D/abstract>`__

Photometric Redshift
--------------------

-  `TPZ: photometric redshift PDFs and ancillary information by using
   prediction trees and random
   forests <https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1483C/abstract>`__

   -  Decision tree and random forest.

Cosmology
---------

-  `ml-in-cosmology - a comprehensive list of published machine learning
   applications to
   cosmology <https://github.com/georgestein/ml-in-cosmology>`__

   -  A beautiful list compiled by `George
      Stein <https://github.com/georgestein>`__
   -  “Each entry contains the paper title, a simple summary of the
      machine learning methods used in the work, and the arxiv link”

-  `MiSTree - Beyond two-point statistics: using the Minimum Spanning
   Tree as a tool for
   cosmology <https://knaidoo29.github.io/mistreedoc/index.html>`__

   -  The `public code can be found
      here <https://github.com/knaidoo29/mistree>`__