  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.
