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Applying Deep Learning to Fast Radio Burst Classification

  • Authors: Liam Connor and Joeri van Leeuwen

2018 The Astronomical Journal 156 256.

  • Provider: AAS Journals

Caption: Figure 5.

Four examples of simulated FRBs injected into real data from the CHIME Pathfinder survey. By combining thousands of these true positives with known false positives, we have built a large labeled training set. The three panels in each subfigure are the frequency–time intensity array of the dedispersed pulse, the frequency-averaged pulse profile, and the DM–time intensity array (top to bottom). Combinations of these data products can be used as inputs to the multi-input neural net described in Section 3.

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