NEOMOD2: NEO Population & Risk Model

• NEOMOD2 is a simulation framework that constructs synthetic NEO populations using debiased survey data and randomized orbital sampling.
• The model integrates orbital trajectories over 150 years to compute impact probabilities and assess retrieval dynamics with statistical precision.
• NEOMOD2 informs planetary defense by calibrating survey completeness and risk metrics, thereby supporting mission planning and hazard mitigation.

The NEOMOD2 NEO Population Model is a modeling and simulation framework employed for quantitative assessments of the near-Earth object (NEO) population, utilized in estimating impact probabilities, retrieval dynamics, risk contextualization, and catalog completeness. NEOMOD2 aggregates survey-derived, debiased population statistics, incorporates advanced orbital evolution physics, and enables probabilistic scenario analyses that can be directly compared to real-world risk metrics. This model is central to contemporary planetary defense studies, notably underpinning recent impact frequency calculations and the comparison of NEO threat with other planetary-scale risks (Nugent et al., 4 Aug 2025).

1. Population Generation and Statistical Basis

NEOMOD2 constructs synthetic NEO populations by randomly sampling orbital elements (semi-major axis, eccentricity, inclination, mean anomaly, longitude of ascending node, argument of perihelion) within ranges inferred from observed distributions. Absolute magnitude $H$ serves as the proxy for object size, with $H = 22$ chosen as the lower bound for objects $\geq 140$ m in diameter (assuming typical albedo values of $\sim$0.14–0.15). Population sizes in recent studies have comprised $5\times10^6$ objects with $H$ in $[10,22]$ (Nugent et al., 4 Aug 2025).

Orbital realizations are fully randomized in terms of phase angles, ensuring the absence of spurious clustering and maximizing coverage of all potentially hazardous orbital configurations. Simulated populations are anchored to current observational completeness estimates—for $H<22$ these are roughly 46%, yielding an inferred complete population of $\sim$22,800 objects from 10,502 known NEOs as of mid-2023. These numbers reflect calibration against the NEO Surveyor Known Object Model (KOM) and debiased ATLAS/CSS results (Grav et al., 2023, Deienno et al., 2024).

2. Orbital Integration and Impact Probability Calculation

The key methodological advance in NEOMOD2 studies is direct trajectory integration using external ephemeris services such as JPL Horizons. For each synthetic NEO, orbital evolution is computed over a 150-year interval. An impact is flagged when the distance between the object and Earth's center falls below Earth's radius.

Quantitative formula: $$P_{\text{imp}} = \frac{N_{\text{impacts}}}{N_{\text{objects}} \times T}$$ With $H = 22$0, $H = 22$1, $H = 22$2 yr, yielding $H = 22$3 yr$H = 22$4 per object. Multiplying by the total modeled population: $H = 22$5 Three impacts in the simulation are subject to Poisson variation, and shifting by $H = 22$6 impacts changes the overall frequency from $H = 22$7 yr$H = 22$8 to $H = 22$9 yr$\geq 140$0—a robust result within the epistemic uncertainty boundaries (Nugent et al., 4 Aug 2025).

3. Debiasing, Survey Completeness, and Catalog Calibration

NEOMOD2 is predicated on comprehensive debiasing of raw survey detections, referencing both ATLAS and CSS survey power, as well as infrared/thermal completeness from NEO Surveyor. Catalog completeness for $\geq 140$1 (large NEAs, $\geq 140$21 km) is estimated at $\geq 140$3 (ATLAS), while for $\geq 140$4 (roughly 140 m) it is $\geq 140$5, numbers slightly lower than CSS values (Deienno et al., 2024). The Known Object Model (KOM) attribute is implemented via per-epoch “field of regard,” brightness limit, and probabilistic detection factors, emulating real-world survey performance (Grav et al., 2023).

Completeness formula: $\geq 140$6 where $\geq 140$7 refers to detected objects in bin $\geq 140$8, and $\geq 140$9 to the population post-debiasing. These corrections recalibrate NEOMOD2 distributions and scale synthetic population estimates.

4. Dynamical Structure and Source Region Physics

Resonant and secular dynamics are fully integrated within NEOMOD2. The model encodes the $\sim$0 secular resonance as the primary source of faint, small NEOs ($\sim$1), and the 3:1 mean motion resonance with Jupiter as the source for larger, brighter NEAs ($\sim$2) (Deienno et al., 2024). Dynamical features are introduced through injection distributions and migration functions matching the empirical semimajor axis structure.

Tidal disruption model outputs (for catastrophic encounters within $\sim$32 Earth radii) are incorporated as “bursts” in the synthetic population, with string-of-pearls fragment orbital elements and steep size-frequency distribution (Schunová et al., 2014). Special calculations calibrate enhanced impact probabilities (%%%%31$H$32%%%% nominal) and short-lived density enhancements for retrieval-friendly targets.

5. Retrieval Metrics and Mission Planning

The NEOMOD2 framework supports evaluation of asteroidal accessibility through retrieval cost parameters ($\sim$6). Easily Retrievable Object (ERO) characterization is achieved by flagging NEOs whose orbits lie proximate to the invariant manifolds associated with Sun–Earth $\sim$7 and $\sim$8 points, admitting transfer to periodic orbits with $\sim$9 m/s (Yárnoz et al., 2013).

Methodologically, transfers combine ballistic heliocentric Lambert arcs (first impulsive $5\times10^6$0) and subsequent maneuvers to stable manifold insertion sections, informed by differential correction algorithms and numerical continuation for Lyapunov/halo orbit families. A catalog of 12 EROs is established, with some requiring optimal transfers as low as 58 m/s, and selection is automatically updated as NEO catalogs expand.

6. Risk Contextualization and Policy Implications

NEOMOD2-derived impact frequencies (%%%%4$H = 22$4%%%%2 yr$5\times10^6$3 for $5\times10^6$4140 m NEOs) are situated within a broader risk framework, directly compared to per capita risks such as lightning strikes or car crashes (Nugent et al., 4 Aug 2025). Table and figure-based contextualizations enable policy makers and planetary defense agencies to assess the rational justification for asteroid detection and deflection investments. The model’s high reliability and cross-validation with observational data provide statistical justification for ongoing survey and hazard mitigation programs.

Planetary defense strategies employ NEOMOD2 not only for estimating global probabilities but also for identifying ongoing gaps due to detection biases (especially for high-velocity and faint NEOs), underlining the need for improved survey collaboration and advanced retrieval mission planning.

7. Model Limitations and Future Directions

The NEOMOD2 model is fundamentally limited by completeness calibration, number statistics in Poisson-limited integration (i.e., low simulated impact counts), and systematic uncertainties in fragmentation source rates. All plausible error sources in debiasing and detection loss—inclination/eccentricity-dependent survey gaps, reference model selection, and synthetic population boundary effects—tend toward underestimation of risk (Heinze et al., 2020, Deienno et al., 2024). Despite these, NEOMOD2 outputs remain consistent with canonical impact frequency estimates, supporting its adoption in next-generation hazard assessment and resource exploitation planning.

A plausible implication is that as survey completeness approaches $5\times10^6$570–80% in the next decade (through NEO Surveyor and followup campaigns), NEOMOD2 modeling will transition from coarse statistical risk context to object-specific risk management and mission selection, further enhancing planetary defense and asteroid retrieval architecture.