Ground-based visible spectroscopy of asteroids to support the development of an unsupervised Gaia asteroid taxonomy

¹ INAF, Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese, Italy
e-mail: alberto.cellino@inaf.it
² Université de la Côte d’Azur – Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Campus Valrose Nice, Nice Cedex 4, France
e-mail: bendjoya@oca.eu
³ Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Boulevard de l’Observatoire, CS34229, 06304 Nice Cedex 4, France
e-mail: Marco.Delbo@oca.eu
⁴ Planetary Science Institute, Tucson, AZ, USA

Received: 24 April 2020
Accepted: 29 July 2020

Abstract

Context. The Gaia mission of the European Space Agency is measuring reflectance spectra of a number to the order of 10⁵ small Solar System objects. A first sample will be published in the Gaia Data Release scheduled for 2021.

Aims. The aim of our work was to test the procedure developed to obtain taxonomic classifications for asteroids based only on Gaia spectroscopic data.

Methods. We used asteroid spectra obtained using the DOLORES (Device Optimised for the LOw RESolution) instrument, a low-resolution spectrograph and camera installed at the Nasmyth B focus of the Telescopio Nazionale Galileo. Because these spectra have a higher spectral resolution than that typical of the Gaia spectra, we resampled them to more closely match the expected Gaia spectral resolution. We then developed a cloning algorithm to build a database of asteroid spectra belonging to a variety of taxonomic classes, starting from a set of 33 prototypes chosen from the 50 asteroids in our observing campaign. We used them to generate a simulated population of 10 000 representative asteroid spectra and employed them as the input to the algorithm for taxonomic classification developed to analyze Gaia asteroid spectra.

Results. Using the simulated population of 10 000 representative asteroid spectra in the algorithm to be used to produce the Gaia asteroid taxonomy at the end of the mission, we found 12 distinct taxonomic classes. Two of them, with 53% of the sample, are dominant. At the other extreme are three classes each with <1% of the sample, and these consist of the previously known rare classes A, D/Ld, and V; 99.1% of the simulated population fall into a single class.

Conclusions. We demonstrated the robustness of our algorithm for taxonomic classification by using a sample of simulated asteroid spectra fully representative of what is expected to be in the Gaia spectroscopic data catalogue for asteroids. Increasingly larger data sets will become available as soon as they are published in the future Gaia data releases, with the next one coming in 2021. This will be exploited to develop a correspondingly improved taxonomy, likely with minor tweaks to the algorithm described here, as suggested by the results of this preliminary analysis.