Indigo Brownhall, University College London; Miles Lifson, The Aerospace Corporation; Giovanni Lavezzi, Massachusetts Institute of Technology; Enrico Zucchelli, Massachusetts Institute of Technology; Mark Moretto, North Carolina State University; Santosh Bhattarai, University College London; Richard Linares, Massachusetts Institute of Technology
Keywords: Space Debris Modelling, Sustainability, LEO, Space Policy
Abstract:
The increasing amount of debris in Low Earth Orbit (LEO), combined with the sustained growth for the demand for LEO orbits, has heightened the need for new research in transdisciplinary policy and regulatory approaches. The growing inactive population raises significant concerns about the long-term sustainability of the orbital environment, as well as the burden it imposes on Space Situational Awareness (SSA) infrastructure. In practical terms, SSA efforts such as collision avoidance, catalog development and maintenance, could all be hampered by both the sheer volume and the uncontrolled growth of space objects. Beyond these technical challenges, there is also a pressing risk to dark-and-quiet night skies, where the brightness and frequency of satellite passes will continue to adversely affect terrestrial astronomy. Within this context, the concept of orbital “carrying capacity” has gained traction, where policymakers, satellite operators and researchers alike want to determine how many objects in LEO can safely be accommodated without escalating collision risk to unsustainable levels. As new constellations are planned, debris models are used to understand the impact on the potential long-term sustainability of LEO. In addition, there is a need for transdisciplinary approaches, including models such as Integrated Assessment Models (IAMs) – which couple socio-economic theory to physical systems – to research new space policy and understand the second-order effects and actors respond to incentives and requirements imposed by policy. In this paper, we present a verification and validation study of two debris evolution models of varying fidelity from the MIT Orbital Capacity Assessment Tool (MOCAT) suite: the Source Sink Evolutionary Model (SSEM) and a higher fidelity Monte-Carlo (MC). The aim is to understand the precision of the SSEM when compared to the MC, and deep dive into the input parameter space to research the computational time vs precision. The comparison will be observed through multiple metrics through the simulation, including, the total mass on orbit, collision counts, and distribution of objects across altitudes. In addition, we implement the Undisposed Mass Per Year (UMPY) metric, which provides a long-term sustainability value to compare results, assessing how much mass fails to deorbit over the simulation timeline. The underlying mechanics of the two debris models are completely different. MC approaches propagate each space object forward in time and at each time step, calculate a probability of collision, assess breakups and fragmentations, add or remove debris, then store the associated outputs and their orbital elements. Computational time and data storage are two substantial challenges, as MC models can take days to weeks to run one launch scenario and are struggling to run large launch predictions. IAMs, and other socio-economic models (including reinforcement learning, AI, agent-based models) are increasingly being proposed and developed for space policy, however, the underlying astrodynamics and debris models are often not the traditional MC approaches due to their computational cost. These models often rely on optimisers at each timestep, which requires hundreds of simulations of the space environment. MOCAT-SSEM, on the other hand, aims to reduce the computational runtime. Instead of tracking individual objects and stochastically calculating collisions, the model treats satellite populations as distributions in orbital shells and species. The probability of collision within each bin and species is estimated with the theory of gases. As the model abstracts away much of the individual object detail, the SSEM can run thousands of times faster even on a personal laptop, making it attractive for policy explorations and “what-if” analyses where large parameter spaces must be explored in short order.
Despite the efficiency benefits, significant questions remain about how precisely low-fidelity debris models can capture the long-term, complex processes that characterise the debris environment. The subtle differences in how collisions are modelled, the reliability of the underlying astrodynamics, and possible non-linear growth of debris can lead to large discrepancies when comparing the SSEM compared to a MC.
Here, we investigate how MOCAT-SSEM’s accuracy and runtime vary with different input parameters, launch models, and configurations of species and shells. This part of the study simulates the practical reality where non-domain specialists or policy researchers might use low fidelity tools without deep familiarity with orbital dynamics. By exploring “misconfigured” scenarios—where model parameters are varied, for example, too few shells or fails to differentiate debris types adequately—we can illustrate potential pitfalls and establish guidelines on how to properly set up SSEM for credible results. We also examine the impact of shorter versus longer simulation timelines, revealing whether certain phenomena (including runaway effects) only become significant at later timescales and how robust the SSEM is to these differences.
We expect to see that MOCAT-SSEM models the long-term debris environment effectively compared to MOCAT-MC. Increasing the complexity of the model (more orbital shells and species) should improve alignment between the models, however, there will be a large computational burden in the SSEM and a tradeoff will be required. Moreover, the models will compare more precisely across shorter timescales and diverge over long-scales.
The significance of this research lies in critically assessing the underlying dynamics of these models to support interdisciplinary research. A well-calibrated SSEM could be transformative, enabling quick policy and design trade-offs that are simply not feasible with full MC simulations. This will continually allow a much broader audience to integrate debris evolution into their work, or integrate more realistic modelling if currently not included. However, if these lower fidelity methods are used improperly, they can mislead critical decisions that could affect orbital debris mitigation, capacity management or dark-and-quiet night skies regulation.
Date of Conference: September 16-19, 2025
Track: Space Debris