Compositional Diversity and Space Weathering of Near-Earth Objects
Authors: Sergeyev Alexey V. , Carry Benoît , Marsset Michaël , Pravec Petr , Perna Davide , DeMeo Francesca E. , Petropoulou Vasiliki , Lazzarin Monica , La Forgia Fiorangela , Di Pietro Ilaria 
Published: July 2024

IntroductionAsteroids, as remnants of the early solar system, hold crucial clues about the processes that led to the formation of planets. These small bodies are composed of materials that have remained relatively unchanged since the solar system&#8217;s formation, making them valuable targets for scientific study. Near-Earth objects (NEOs), which include both asteroid-like and comet-like bodies whose orbits bring them close to Earth, are particularly important. These objects not onlyoffer insights into planetary formation but also pose potential threats to Earth due to their proximity and frequent interactions with terrestrial planets. Understanding their composition, distribution, and behavior is essential for both scientific research and planetary defense.
Data Collection

Fig. 1: Orbital distribution of Near-Earth Objects by type, showing semimajor axis vs. eccentricity (left) and perihelion vs. inclination (right).

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Objective and Methodology
We gather visible colors of NEOs from several astronomical surveys, including the Sloan Digital Sky Survey (SDSS) (Sergeyev &amp; Carry, 2021), SkyMapper (Sergeyev et al., 2022), Gaia mission (Galluccio et al., 2022), and ground-based observations (Mahlke et al., 2022). These datasets are merged into a single catalog, creating a comprehensive resource for analyzing the compositional properties of NEOs. The orbital distribution of NEOs in our study is shown in Fig. 1. Each data source offers unique contributions:
&#8211; SDSS: Provides multi-filter observations in u, g, r, i, z bands, allowing for detailed photometric analysis.&#8211; SkyMapper: Offers a combination of shallow and deep sequences in multiple filters, enhancing the dataset&#8217;s depth and breadth.&#8211; Gaia: Contains low-resolution reflectance spectra covering a wide wavelength range, providing crucial spectral data.&#8211; Ground-based Observations: High-resolution spectra from various surveys add to the robustness of the dataset.
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Data ProcessingData from these diverse sources were cross-matched and compared to ensure consistency. Systematic biases were identified and corrected to create a homogeneous dataset. Given the fast-moving nature of NEOs, the study re-measured photometry for these objects in the SDSS to address potential biases related to their rapid motion. This step was crucial for ensuring accurate photometric measurements, which are foundational for subsequent analysis.
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Taxonomy and Classification
The taxonomy of NEOs was determined using photometric colors, which were converted from reflectance spectra. The classification followed a probabilistic approach, assigning each NEO to one of ten taxonomic classes (A, B, C, D, K, L, Q, S, V, X) based on the highest probability. This methodology allows for a comprehensive classification scheme that accommodates the inherent uncertainties in photometric data see Fig. 2.&#8211; Multi-color Classification: Utilized combinations of g, r, i, z colors to classify NEOs with high accuracy.&#8211; Single-color Classification: Applied when only g-r color was available, providing a broader classification into "red" or "blue" objects. This approach, while less precise, ensures that all available data can be utilized.

Fig. 2: Taxonomic classification of NEOs in the SDSS color space.

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Results
The study produced several key findings:&#8211; Photometric and Taxonomic Data: The catalog includes updated photometry for 470 NEOs and taxonomic classifications for 7,401 NEOs (Sergeyev et al., 2023) see Table 1. This extensive dataset forms a solid foundation for further analysis.&#8211; Spectral Slope and Perihelion Dependence: Confirmed the relationship between spectral slope and perihelion among S-type NEOs, suggesting a rejuvenation mechanism linked to thermal fatigue. This finding supports existing theories about the effects of solar radiation on asteroid surfaces (Graves et al., 2019).
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Analysis of Space Weathering
Space weathering, which alters the surface properties of asteroids through exposure to solar wind and micrometeorite impacts, was analyzed using spectral slope and taxonomic distribution. This analysis provides insights into the aging processes of asteroid surfaces.&#8211; S-type Asteroids: Showed a constant spectral slope for smaller diameters and an increase for larger ones, consistent with previous studies. This trend indicates that space weathering affects asteroids differently based on their size.
&#8211; Q/S Ratio: Indicated a higher fraction of Q types (fresh surfaces) among smaller NEOs, suggesting a size-dependent space weathering process see Fig 3. This ratio is an important indicator of the relative age of asteroid surfaces.
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Fig. 3: Running mean of the ratio between the number of Q and S asteroids as a function of perihelion, inclination, and diameter. Shaded areas correspond to the uncertainties considering the Poisson statistic for the Q/S ratio.

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Distribution of A-type AsteroidsA-types, characterized by olivine-rich compositions, are rare in the main belt but more common among NEOs. The study found a higher fraction of A-types near the orbit of Mars, possibly linked to the Hungaria asteroid family (Devog&#232;le et al., 2019). This distribution pattern provides clues about the dynamical processes that bring these objects into near-Earth space.
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Source Regions of NEOs
The study predicted the taxonomic distribution of small asteroids in various source regions, such as the &#957;6 secular resonance, 3:1 and 2:1 mean-motion resonances with Jupiter, Phocaea, and Hungaria regions, and Jupiter Family Comets (JFC). The results align with existing models, showing the dominance of mafic-silicate-rich asteroids in inner regions and opaque-rich asteroids in outer regions. This distribution reflects the compositional gradients in the asteroid belt and the dynamical processes that transport these objects (Marsset et al., 2022).
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References
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