Predicting Surface Temperatures on Airless Bodies: An Open-Source Python Tool

• DOI: 10.5194/epsc2024-1121
• Publication Date: 2024-07-03T12:38:20Z
• Authors: Duncan Lyster; Carly Howett; Joseph Penn
• Keywords: Python

Abstract:Understanding the thermal properties of comets, asteroids, and icy moons is crucial for advancing our knowledge their composition evolution. An increasing number missions to these bodies, including Rosetta, Osiris-Rex, Europa Clipper, JUICE, necessitate models that accurately consider complex topographies.We have developed a Python model predicts diurnal temperature variations on airless bodies in three dimensions, factoring morphologies. This significantly improves simulation accuracy by incorporating shadowing effects. The results are consistent with established such as one-dimensional conduction J.R. Spencer, thermprojrs [1], three-dimensional surface energy balance Guilbert-Lepoutre & Jewitt [2].Our tool was built support NASA Lucy Mission ESA Comet Interceptor mission. However, it suited wide range targets, those active surfaces. user-friendly operates quickly, running seconds basic shape models, requiring only few minutes comet 67P 16,000 facets. It designed handle subsurface heating efficiently parallelised, enabling applications large, high spatial resolution models.Background:The behaviour planetary governed properties, albedo, roughness, inertia. Accurate essential interpreting observational data planning missions. Traditional Spencer's thermprojrs, provided valuable insights into evolution simulating heat conduction. These instrumental understanding seasonal surface.However, complexity topographic features, craters, ridges, boulders, necessitates more advanced modelling techniques. While existing addressed some complexities, there remains need accessible, high-resolution simulate interpret from current upcoming accurately.Outline Model:Our open-source Python-based aims fill this gap providing comprehensive can account morphological features enhances predictions makes techniques available wider scientific community. integration mapping further refine leading improved interpretations bodies.The solves equation each facet file. factors solar heating, radiative loss, conduction, heating. result map evolves over time, cycle target body under various conditions.This approach efficient user-friendly, process thousands model's parallelised nature allows rapid computation, making suitable extensive simulations analyses.Validation Work:To validate model, we compared its output model. As shown Figure 1, versus time profiles both show close agreement, slight differences attributed handling deep layer temperatures. Figure 1: Temperature vs Time single facet. Comparison between Spencer model.The has been tested asteroid Bennu. inclusion effects, areas significant variation (Figure 2).Figure 2: 3D illumination effects sphere, Bennu, 67P.The generated 3), highlights distribution across surface. maps mission operations. could also be used tandem enhancing detailed bodies.Figure 3: Thermal showing 67P.Conclusion:Our represents an improvement bodies. provides accurate runs applications, realistic instrument planning.Planned improvements include implementing self-heating, scattering incident light, loss through sublimation. Extensive parameter sensitivity analysis will enhance applicability diverse targets.The code open source, contributions welcome:github.com/duncanLyster/comet_nucleus_model  References:Spencer, J.R., Lebofsky, L.A., Sykes, M.V., 1989. Systematic biases radiometric diameter determinations. Icarus, 78(2), pp.337-354. Guilbert-Lepoutre, A., Jewitt, D., 2011. shadows compositional structure nuclei. Astrophysical Journal, 743(1), p.31.