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Executive Summary

The Fornax Initiative is a NASA Astrophysics Archives project to collaboratively among the three archives HEASARC, IRSA, and MAST, create cloud systems, cloud software, and cloud standards for the astronomical community.

The Fornax Science Console is a cloud compute system near to NASA data in the AWS cloud which provides a place where astronomers can do data intensive research with reduced barriers. The Fornax Initiative provides increased compute, increased memory, increased ease of use by pre-installing astronomical software, increased reprododicibility of big data results, increased inclusion by removing some of these barriers to entry, tutorial notebooks, and documentation.

This repo houses tutorial notebooks of fully worked science use cases for all users. Common goals of the notebooks are the use of archival data from all NASA archives, cross-archive work, big data, and computationally intensive science. Currently there are two major topics for which we have notebooks. The "Photometry" notebook starts with a catalog and a set of archival images. The notebook grabs all necesary images and measures photometry at all positions listed in the catalog on all images. The "Time Domain" notebooks are twofold. The first generates light curves from all available archival data for any user supplied sample of targets. The second notebook runs ML algorithms to classify those generated light curves.

Content contributing

In this repository we use Jupytext and MyST Markdown Notebooks. You will need jupytext installed for your browser to recognise the markdown files as notebooks (see more about the motivation and technicalities e.g. here: https://numpy.org/numpy-tutorials/content/pairing.html).

If you already have an ipynb file, convert it to Markdown using the following command, and commit only the markdown file to the repo:

jupytext --from notebook --to myst yournotebook.ipynb

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classifier to determine variable vs. non-variable AGN based on WISE W1 light curves

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  • Python 100.0%