1. Developing a Synthetic Light Curve Forward Model
Laurence Blacketer
Astronautics Research Group
University of Southampton

2. Introduction
PhD at the University of Southampton, with Dr. Hugh Lewis, under the preliminary title “Attitude Detection from Light Curves”
NASA requests of the IADC member states light curve data of large objects, in support of ADR research.
Following a brief analysis, the results data were found to be inconclusive.

3. The Data ESA: - University of Bern - Zimmerwald telescope
JAXA: - Nyukasayama Optical Observatory
CNSA: - Multiple Sensors
NASA: - TOP SECRET

4. MMT The Mini-MegaTORTORA system 9 channels installed in pairs
5630 objects
157,869 light curves
Oldest: 2014/06/04
Latest: 2018/03/06

5. The Model
Synthetic forward model, used to replicate true light curve data.
Positional data acquired from the NASA Horizons system and TLE database.
Brightness measurements are generated through application of Bidirectional Reflectance Function, to a faceted geometry.

6. Model Inputs Inputs: Light curve start and stop times. Object Geometry
BDRF parameters
Attitude State

7. Model Output

8. Demonstration: An Active System
Object: Globalstar M067 [32264]
Launched:
Current Status : ACTIVE
Attitude: [0, 0, 0]
Attitude Motion: [0, 0, 0]
BDRF: - Primarily diffuse reflection from body
- Primarily specular reflection from panels

9. Active: Average Magnitudes

10. Active: Standard Deviations

11. Overlaid: Avg. Magnitudes

12. Overlaid: Standard Deviations

13. An Inactive System Object: Globalstar M054 [25885]
Launched:
Current Status : INACTIVE
MMT Period: 7.15s
Attitude: [0.4449, , ] rad
Attitude Motion: [0.7406, , ] rad

14. Inactive: Average Magnitudes

15. Inactive: Standard Deviations

16. Comparison:

17. Object of Interest Object: Globalstar M067 [32263]
Launched:
Current Status : INACTIVE
Attitude: [0, 0, 0]
Attitude Motion: [0, 0, 0]

18. Object of Interest: Avg. Magnitudes

19. Object of Interest: Standard Deviations

20. Overlaid Standard Deviations

21. Results Summary
Modelling an active system with zero attitude motion produces light curves with similar properties to real data.
The inactive system was modelled with a random attitude motion, the period of which matches the MMT data. Although Std. Devs. are higher in the synthetic data, both are larger than in the active case.
The Std. Devs. of the object of interest transition from values typical of an active system, to values typical of an inactive system.

22. Future Work Expanded and more rigorous variability analysis.
Writing a paper on the sensitivity of the synthetic light curve model to varying inputs.
Inverse model development. Use the light curve data to produce attitude state estimates.

23. Thank you for listening.
Laurence Blacketer – University of Southampton