Last edited on 2023-04-14 Tagged under  #space 

Here are this week's 3 links worth exploring:

  1. An open source machine learning model called Deep leArninG Geomagnetic pErtuRbation (DAGGER) has been developed to predict potentially dangerous solar storms. Using a combination of space weather sensors monitoring solar activity, geomagnetic perturbations observed at ground stations across the planet, and artificial intelligence (AI) tools, its developers claim it can give a 30 minute warning ahead of time of dangerous space weather, giving operators of critical Earth and space infrastructure time to batten down the hatches: https://www.nasa.gov/feature/goddard/2023/sun/nasa-enabled-ai-predictions-may-give-time-to-prepare-for-solar-storms

  2. DAGGER model is built using a wide range of Python tools: Pytorch, Sympy, Numpy and Scipy for analysis, Dask and Pandas for data processing, Matplotlib and Cartopy for plotting. For getting started with their code, DAGGER developers have made a tutorial available as a Jupyter notebook: https://github.com/spaceml-org/helionb-geoeff

  3. SpaceML is where deep learning meets deep space. Its mission is to enable citizen scientists, scholars, and hobbyists alike by providing access to machine learning community projects, open source code, and maintained benchmark datasets: https://spaceml.org/

Quote of the Week: "Unless people can see broad vistas of unused resources in front of them, the belief in limited resources tends to follow as a matter of course. And if the idea is accepted that the world's resources are fixed, then each person is ultimately the enemy of every other person, and each race or nation is the enemy of every other race or nation. The extreme result is tyranny, war and even genocide. Only in a universe of unlimited resources can all men be brothers." — Robert Zubrin, The Case For Mars


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