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This work is dedicated to debias the Near-Earth Object (NEO) population based on observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS) telescopes. We have applied similar methods used to develop the recently released NEO model generator (NEOMOD), once debiasing the NEO population using data from Catalina Sky Survey (CSS) G96 telescope. ATLAS is composed of four different telescopes. We first analyzed observational data from each of all four telescopes separately and later combined them. Our results highlight main differences between CSS and ATLAS, e.g., sky coverage and survey power at debiasing the NEO population. ATLAS has a much larger sky coverage than CSS, allowing it to find bright NEOs that would be constantly ‘‘hiding’’ from CSS. Consequently, ATLAS is more powerful than CSS at debiasing the NEO population for H ≲ 19. With its intrinsically greater sensitivity and emphasis on observing near opposition, CSS excels in the debiasing of smaller objects. ATLAS, as an all sky survey designed to find imminent hazardous objects, necessarily spends a significant fraction of time looking at places on the sky where objects do not appear, reducing its power for debiasing the population of small objects. We estimate a NEO population completeness of ≈ 88%+3% −2% for H < 17.75 and ≈ 36%+1% −1% for H < 22.25. Those numbers are similar to previous estimates (within error bars for H < 17.75) from CSS, yet, around 3% and 8% smaller at their face values, respectively. We also confirm previous finding that the 𝜈6 secular resonance is the main source of small and faint NEOs at H = 28, whereas the 3:1 mean motion resonance with Jupiter dominates for larger and brighter NEOs at H = 15. 

Our previous model (NEOMOD2) for the orbital and absolute magnitude distribution of Near Earth Objects (NEOs) was calibrated on the Catalina Sky Survey observations between 2013 and 2022. Here we extend NEOMOD2 to include visible albedo information from the Wide-Field Infrared Survey Explorer. The debiased albedo distribution of NEOs can be approximated by the sum of two Rayleigh distributions with the scale parameters 𝑝V,dark ≃ 0.03 and 𝑝V,bright ≃ 0.17. We find evidence for smaller NEOs having (on average) higher albedos than larger NEOs; this is likely a consequence of the size-dependent sampling of different main belt sources. These inferences and the absolute magnitude distribution from NEOMOD2 are used to construct the debiased size distribution of NEOs. We estimate 830±60 NEOs with diameters 𝐷 > 1 km and 20,000±2,000 NEOs with 𝐷 > 140 m. The new model, NEOMOD3, is available via the NEOMOD Simulator — an easy-to-operate code that can be used to generate user-defined samples (orbits, sizes and albedos) from the model. 

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% 

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.