Taxonomy of Subkilometer Near-Earth Objects from Multiwavelength Photometry with RATIR

• Published in: The Astronomical journal Vol. 167; no. 4; pp. 163 - 172
• Main Authors: Navarro-Meza, S., Trilling, D. E., Mommert, M., Butler, N., Reyes-Ruiz, M.
• Format: Journal Article
• Language: English
• Published: Madison The American Astronomical Society 01.04.2024; IOP Publishing
• Subjects: Algorithms; Asteroids; Classification; Earth; Infrared analysis; Monte Carlo simulation; Near-Earth Objects; Photometric observations; Photometry; Small Solar System bodies; Spectral analysis; Spectrophotometry; Spectrum analysis; Taxonomy; Telescopes
• ISSN: 0004-6256; 1538-3881
• DOI: 10.3847/1538-3881/ad23d0

Abstract We present results from observations of 238 near-Earth objects (NEOs) obtained with the RATIR instrument on the 1.5 m robotic telescope at San Pedro Martir’s National Observatory in Mexico, in the frame of our multiobservatory, multifilter campaign. Our project is focused on rapid response photometric observations of NEOs with absolute magnitudes in the range 18.1–27.1 (diameter ≈ 600 and 10 m, respectively). Data with coverage in the near-infrared and visible range were analyzed with a nonparametric classification algorithm, while visible-only data were independently analyzed via Monte Carlo simulations and a 1-Nearest Neighbor method. The rapid response and the use of spectrophotometry allows us to obtain taxonomic classifications of subkilometer objects with small telescopes, representing a convenient characterization strategy. We present taxonomic classifications of the 87 objects observed in the visible and near-infrared. We also present the taxonomic distribution of an additional 151 objects observed in the visible. Our most accurate method suggests a nonfeatured-to-featured ratio of ≈0.75, which is consistent with the value found by the Mission Accessible Near-Earth Object Survey, which conducted a similar study using a spectral analysis. The results from the Monte Carlo method suggest a ratio of ≈0.8, although this method has some limitations. The 1-Nearest Neighbor method showed to be not suitable for NEO classifications.