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Automatic detection of impact craters on Al foils from the Stardust interstellar dust collector using convolutional neural networks

Jaeger, Logan, Butterworth, Anna L., Gainsforth, Zack, Lettieri, Robert, Zevin, Daniel, Ardizzone, Augusto, Capraro, Michael, Burchell, Mark J., Wozniakiewicz, Penelope J., Ogliore, Ryan, and others. (2021) Automatic detection of impact craters on Al foils from the Stardust interstellar dust collector using convolutional neural networks. Meteoritics & Planetary Science, 56 (10). pp. 1890-1904. ISSN 1086-9379. (doi:10.1111/maps.13747) (KAR id:92698)

Abstract

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

Item Type: Article
DOI/Identification number: 10.1111/maps.13747
Uncontrolled keywords: Stardust, craters, impacts
Subjects: Q Science > QB Astronomy > QB651 Planets, Minor
Divisions: Divisions > Division of Natural Sciences > Physics and Astronomy
Depositing User: Mark Burchell
Date Deposited: 14 Jan 2022 15:00 UTC
Last Modified: 05 Nov 2024 12:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92698 (The current URI for this page, for reference purposes)

University of Kent Author Information

Burchell, Mark J..

Creator's ORCID: https://orcid.org/0000-0002-2680-8943
CReDIT Contributor Roles:

Wozniakiewicz, Penelope J..

Creator's ORCID: https://orcid.org/0000-0002-1441-4883
CReDIT Contributor Roles:
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