AUTHOR=Penttilä A. , Fedorets G. , Muinonen K. 

TITLE=Taxonomy of Asteroids From the Legacy Survey of Space and Time Using Neural Networks

JOURNAL=Frontiers in Astronomy and Space Sciences

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2022.816268

DOI=10.3389/fspas.2022.816268

ISSN=2296-987X

ABSTRACT=<p>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.</p>