Ground-based visible spectroscopy of asteroids to support the development of an unsupervisedGaiaasteroid taxonomy

Abstract

Context.TheGaiamission of the European Space Agency is measuring reflectance spectra of a number to the order of 105small Solar System objects. A first sample will be published in theGaiaData Release scheduled for 2021.Aims.The aim of our work was to test the procedure developed to obtain taxonomic classifications for asteroids based only onGaiaspectroscopic 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 NazionaleGalileo. Because these spectra have a higher spectral resolution than that typical of theGaiaspectra, we resampled them to more closely match the expectedGaiaspectral 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 analyzeGaiaasteroid spectra.Results.Using the simulated population of 10 000 representative asteroid spectra in the algorithm to be used to produce theGaiaasteroid 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 theGaiaspectroscopic data catalogue for asteroids. Increasingly larger data sets will become available as soon as they are published in the futureGaiadata 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.