Taxonomy of Asteroids From the Legacy Survey of Space and Time Using Neural Networks
| Autor: | A. Penttilä, G. Fedorets, K. Muinonen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Frontiers in Astronomy and Space Sciences, Vol 9 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2296-987X 86252208 |
| DOI: | 10.3389/fspas.2022.816268 |
| Popis: | The Legacy Survey of Space and Time (LSST) is one of the ongoing or future surveys, together with the Gaia and Euclid missions, which will produce a wealth of spectrophotometric observations of asteroids. This article shows how deep learning techniques with neural networks can be used to classify the upcoming observations, particularly from LSST, into the Bus-DeMeo taxonomic system. We report here a success ratio in classification up to 90.1% with a reduced set of Bus-DeMeo types for simulated observations using the LSST photometric filters. The scope of this work is to introduce tools to link future observations into existing Bus-DeMeo taxonomy. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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