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TensorFlow Machine Learning model to better interpret and analyze radio array telescope big data. ...learn more
Project status: Under Development
Groups
TensorFlow,
Student Developers for AI
Intel Technologies
AI DevCloud / Xeon,
Intel Opt ML/DL Framework
For decades, institutional data on radio telescope arrays have accumulated in American astronomical research institutions.
Radio array telescopes like SETI at Home and the NRAO Very Large Array have over time, amassed big, not just large, big data sets of astronomical data concerning the cosmos. Data points such as polarization, radio wave frequency, magnitude, longitude/latitude, and date/time help astronomers pinpoint and understand stars.
We seek to use TensorFlow's integration with the Intel DevCloud to construct a TensorFlow Machine Learning model which better interprets and analyzes radio telescope array big data and also to utilize machine vision to further enhance radio images created by astronomical big data.
TensorFlow. Intel DevCloud.