Automatic detection of impact craters on Al foils from the Stardust interstellar dust collector using convolutional neural networks
| Autor: | Logan Jaeger, Anna L. Butterworth, Zack Gainsforth, Robert Lettieri, Dan Zevin, Augusto Ardizzone, Michael Capraro, Mark Burchell, Penny Wozniakiewicz, Ryan C. Ogliore, Bradley T. De Gregorio, Rhonda M. Stroud, Andrew J. Westphal, Donald Brownlee |
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| Rok vydání: | 2021 |
| Předmět: |
Earth and Planetary Astrophysics (astro-ph.EP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer science FOS: Physical sciences Convolutional neural network Machine Learning (cs.LG) Astrobiology Geophysics Impact crater Space and Planetary Science QB651 Astrophysics - Instrumentation and Methods for Astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) Astrophysics - Earth and Planetary Astrophysics Cosmic dust |
| Zdroj: | Meteoritics & Planetary Science. 56:1890-1904 |
| ISSN: | 1945-5100 1086-9379 |
| DOI: | 10.1111/maps.13747 |
| Popis: | NASA's Stardust mission utilized a sample collector composed of aerogel and aluminum foil to return cometary and interstellar particles to Earth. Analysis of the aluminum foil begins with locating craters produced by hypervelocity impacts of cometary and interstellar dust. Interstellar dust craters are typically less than one micrometer in size and are sparsely distributed, making them difficult to find. In this paper, we describe a convolutional neural network based on the VGG16 architecture that achieves high specificity and sensitivity in locating impact craters in the Stardust interstellar collector foils. We evaluate its implications for current and future analyses of Stardust samples. |
| Databáze: | OpenAIRE |
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