Dr. Maximilian Hamm
Postdoc in Geo.DataScience
#Asteroids #Data Assimilation #Space Exploration
Uncertainty Quantification of Asteroid Surface Thermal Properties
Collaborators: Prof. Dr. Matthias Holschneider (UP), Dr. Jana de Wiljes (UP) and Dr. Matthias Grott (DLR)
Project Outline
Asteroids are among the most pristine objects in the solar system and remnants of its formation. At the same time Near-Earth Asteroids can cross the Earth orbit with potentially catastrophic consequences. The structure of the surface of asteroids does not only give insights on the formation of the asteroid and with it of the solar system, it is also crucial for designing any kind of interaction with the asteroid surface, e.g. sampling, landing or deflection missions. Mechanical and thermal properties of the surface material are linked, since the thermal conductivity of a material depends on its structure, e.g. the porosity. The thermal conductivity can be derived by observing the temperature evolution of a surface as a response to solar illumination. The temperature evolution can be determined from measured thermal-infrared fluxes, either from Earth-based telescopes or by visiting spacecraft, e.g. Hayabusa2, MASCOT, OSIRIS-REX
Inferring the thermal properties of an asteroid from the thermal infrared observation is difficult due to the fact that running a single model for a given parameter set can take many hours on a normal CPU. This hinders the use of parameter retrieval algorithms where usually thousands of parameter sets are sampled from the parameter space. As a consequence only few parameters are actually fitted to the data while most parameters are considered constants or evaluated at few points only.
In this project I am developing new techniques to accelerate the thermal simulations. Furthermore, I apply modern statistical methods from the field of data assimilation, e.g. Ensemble Square Root Filter. These methods can efficiently handle high-dimensional problems and retrieve multiple material properties, their uncertainties and correlations. These methods constitute a major advancement over the state of art, both in speed of the analysis and its quality.
Publications
Hamm, M., Pelivan, I., Grott, M., De Wiljes, J., 2020, Thermophysical modelling and parameter estimation of small solar system bodies via data assimilation, submitted to MNRAS
Okada, T., Fukuhara, T., Tanaka, S., Taguchi, M., Arai, T., Senshu, H., Sakatani, N., Shimaki, Y., Demura, H., Ogawa, Y., Suko, K., Sekiguchi, T., Kouyama, T., Takita, J., Matsunaga, T., Imamura, T., Wada, T., Hasegawa, S., Helbert, J., Müller, T., Hagermann, A., Biele, J., Grott, M., Hamm, M., et al., Highly porous nature of a primitive asteroid revealed by thermal imaging, Nature, 2020, accepted, https://doi.org/10.1038/s41586-020-2106-6
Grott, M., Knollenberg, J., Hamm, M., Ogawa, K., Jaumann, R., Otto, K. A., et al., Low thermal conductivity boulder with high porosity identified on C-type asteroid (162173) Ryugu, 2019, Nature Astronomy, https://doi.org/10.1038/s41550-019-0832-x
Hamm, M., Senshu, H., Grott, M., Latitudinal Dependence of Asteroid Regolith Formation by Thermal Fatigue, 2019, Icarus, , 319, pp. 308-311, https://doi.org/10.1016/j.icarus.2018.09.033
Ogawa, K., Hamm, M., Grott, M., Sakatani, N., Knollenberg, J., Biele, J., Possibility of estimating particle size and porosity on Ryugu through MARA temperature measurements, 2019, Icarus, 333, pp. 318-322, https://doi.org/10.1016/j.icarus.2019.06.014
Biele, J., Kührt, E., Senshu, H., Sakatani, N., Ogawa, K., Hamm, M., Grott, M., Okada, T., Arai, T., Effects of dust layers on thermal emission from airless bodies, 2019, Progress in Earth and Planetary Science, 6, 48, https://doi.org/10.1186/s40645-019-0291-0
Hamm, M., Grott, M., Kührt, E., Pelivan, I., Knollenberg, J., A Method to Derive Surface Thermophysical Properties of Asteroid (162173) Ryugu (1999JU3) from In-Situ Surface Brightness Temperature Measurements, Planetary and Space Science, 2018, 159, 1-10, https://doi.org/10.1016/j.pss.2018.03.017
Methods
- Data Assimilation
- Regression
- Uncertainty Quantification
- Thermophysical Modelling
Programming
C, MATLAB, IDL, python
ML algorithms
Ensemble Square Root Filter
Tools
spice, Jupyter Notebook, Paraview
Libraries
NAG, NumPy, SciPy, SPICE