More Like this

1. NEOMOD 2: An updated model of Near-Earth Objects from a decade of Catalina Sky Survey observations

   https://doi.org/10.1016/j.icarus.2023.115922  

   Nesvorný, David; Vokrouhlický, David; Shelly, Frank; Deienno, Rogerio; Bottke, William F; Christensen, Eric; Jedicke, Robert; Naidu, Shantanu; Chesley, Steven R; Chodas, Paul W; et al (March 2024, Icarus)

   NA (Ed.)

   Catalina Sky Survey (CSS) is a major survey of Near-Earth Objects (NEOs). In a recent work, we used CSS observations from 2005–2012 to develop a new population model of NEOs (NEOMOD). CSS’s G96 telescope was upgraded in 2016 and detected over 10,000 unique NEOs since then. Here we characterize the NEO detection efficiency of G96 and use G96’s NEO detections from 2013–2022 to update NEOMOD. This resolves previous model inconsistencies related to the population of large NEOs. We estimate there are 936 ± 29 NEOs with absolute magnitude 𝐻 < 17.75 (diameter 𝐷 > 1 km for the reference albedo 𝑝V = 0.14) and semimajor axis 𝑎 < 4.2 au. The slope of the NEO size distribution for 𝐻 = 25–28 is found to be relatively shallow (cumulative index ≃ 2.6) and the number of 𝐻 < 28 NEOs (𝐷 > 9 m for 𝑝V = 0.14) is determined to be (1.20 ± 0.04) × 107 , about 3 times lower than in Harris & Chodas (2021). Small NEOs have a different orbital distribution and higher impact probabilities than large NEOs. We estimate 0.034 ± 0.002 impacts of 𝐻 < 28 NEOs on the Earth per year, which is near the low end of the impact flux range inferred from atmospheric bolide observations. Relative to a model where all NEOs are delivered directly from the main belt, the population of small NEOs detected by G96 shows an excess of low-eccentricity orbits with 𝑎 ≃ 1–1.6 au that appears to increase with 𝐻 (≃ 30% excess for 𝐻 = 28). We suggest that the population of very small NEOs is boosted by tidal disruption of large NEOs during close encounters to the terrestrial planets. When the effect of tidal disruption is (approximately) accounted for in the model, we estimate 0.06 ± 0.01 impacts of 𝐻 < 28 NEOs on the Earth per year, which is more in line with the bolide data. The impact probability of a 𝐻 < 22 (𝐷 > 140 m for 𝑝V = 0.14) object on the Earth in this millennium is estimated to be ≃ 4.5% 

   more » « less
2. An ALMA Search for High-albedo Objects Among the Midsized Jupiter Trojan Population

   https://doi.org/10.3847/1538-3881/ac559e  

   Simpson, Anna M.; Brown, Michael E.; Schemel, Madeline J.; Butler, Bryan J. (June 2022, The Astronomical Journal)

   Abstract We use Atacama Large Millimeter Array (ALMA) measurements of 870μm thermal emission from a sample of midsized (15–40 km diameter) Jupiter Trojan asteroids to search for high-albedo objects in this population. We calculate the diameters and albedos of each object using a thermal model which also incorporates contemporaneous Zwicky Transient Facility photometry to accurately measure the absolute magnitude at the time of the ALMA observation. We find that while many albedos are lower than reported from WISE, several small Trojans have high albedos independently measured both from ALMA and from WISE. The number of these high-albedo objects is approximately consistent with expectations of the number of objects that recently have undergone large-scale impacts, suggesting that the interiors of freshly-crated Jupiter Trojans could contain high-albedo materials such as ices. 

   more » « less
3. Using Neural Networks to Model Main Belt Asteroid Albedos as a Function of Their Proper Orbital Elements

   https://doi.org/10.3847/PSJ/acd381  

   Murray, Zachary (May 2023, The Planetary Science Journal)

   Abstract Asteroid diameters are traditionally difficult to estimate. When a direct measurement of the diameter cannot be made through either occultation or direct radar observations, the most common method is to approximate the diameter from infrared observations. Once the diameter is known, a comparison with visible light observations can be used to find the visible geometric albedo of the body. One of the largest data sets of asteroid albedos comes from the NEOWISE mission, which measured asteroid albedos both in the visible and infrared. We model these albedos as a function of proper orbital elements available from the Asteroid Families Portal using an ensemble of neural networks. We find that both the visible and infrared geometric albedos are significantly correlated with asteroid position in the belt and occur in both asteroid families and in the background belt. We find that the ensemble’s prediction reduces the average error in the albedo by about 37% compared to a model that simply adopts an average albedo with no regard for the dynamical state of the body. We then use this model to predict albedos for the half million main belt asteroids with proper orbital elements available in the Asteroid Families Portal and provide the results in a catalog. Finally, we show that several presently categorized asteroid families exist within much larger groups of asteroids of similar albedos—this may suggest that further improvements in family identification can be made. 

   more » « less
4. NEOMOD: A New Orbital Distribution Model for Near-Earth Objects

   https://doi.org/10.3847/1538-3881/ace040  

   Nesvorný, David; Deienno, Rogerio; Bottke, William F.; Jedicke, Robert; Naidu, Shantanu; Chesley, Steven R.; Chodas, Paul W.; Granvik, Mikael; Vokrouhlický, David; Brož, Miroslav; et al (July 2023, The Astronomical Journal)

   Abstract Near-Earth Objects (NEOs) are a transient population of small bodies with orbits near or in the terrestrial planet region. They represent a mid-stage in the dynamical cycle of asteroids and comets, which starts with their removal from the respective source regions—the main belt and trans-Neptunian scattered disk—and ends as bodies impact planets, disintegrate near the Sun, or are ejected from the solar system. Here we develop a new orbital model of NEOs by numerically integrating asteroid orbits from main-belt sources and calibrating the results on observations of the Catalina Sky Survey. The results imply a size-dependent sampling of the main belt with the ν 6 and 3:1 resonances producing ≃30% of NEOs with absolute magnitudes H = 15 and ≃80% of NEOs with H = 25. Hence, the large and small NEOs have different orbital distributions. The inferred flux of H < 18 bodies into the 3:1 resonance can be sustained only if the main-belt asteroids near the resonance drift toward the resonance at the maximal Yarkovsky rate (≃2 × 10 −4 au Myr −1 for diameter D = 1 km and semimajor axis a = 2.5 au). This implies obliquities θ ≃ 0° for a < 2.5 au and θ ≃ 180° for a > 2.5 au, both in the immediate neighborhood of the resonance (the same applies to other resonances as well). We confirm the size-dependent disruption of asteroids near the Sun found in previous studies. An interested researcher can use the publicly available NEOMOD Simulator to generate user-defined samples of NEOs from our model. 

   more » « less
5. Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters

   https://doi.org/10.3390/rs15102684  

   Chen, Hao; Lin, Xingwen; Sun, Yibo; Wen, Jianguang; Wu, Xiaodan; You, Dongqin; Cheng, Juan; Zhang, Zhenzhen; Zhang, Zhaoyang; Wu, Chaofan; et al (May 2023, Remote Sensing)

   High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472. 

   more » « less