In the near future, direct imaging missions will search for Earth-like planets around nearby stars. One of the problems is how to characterize the planet surface. To address this question, we are developing a surface map and components reconstruction method using a one-dimensional light curve of a direct-imaged planet. The orbital motion and spin rotation of a planet conveys information about the spherical surface to the time-series of the light curve. In the future, this theoretical work will be tested in the era of space direct imaging of exoplanets. See wiki for further description.
Spin-Orbit Tomography (SOT) is a retrieval technique of a 2 dimensinal map of an Exo Earth from time-series data of integrated reflection light.
python setup.py install
Jupyter notebook based tutorial. See sot/tutorial
- sotl2.ipynb -- L2-SOT from scratch
- sotl2_sklearn_ridge.ipynb -- using Scikit-Learn ridge
- sotl2_pytorch.ipynb -- using an ADAM optimizer in pytorch
- sotl2_jax.ipynb -- using an ADAM optimizer in JAX
- sot_Bayesian.ipynb -- Bayesian static SOT
- dynamic SOT for a rotating Earth.ipynb -- dynamic SOT (point estimate)
See sot/bin
- dysot_pest -- Point estimate of time-varying map using dynamic SOT
- dysot_sampling -- Sampling geometric parameters for Bayesian dynamic SOT
- dysot_bayesmap -- Bayesian dynamic mapping using the sampling of geometric parameters
- stsou_pest_qf -- Static spin-orbit unmixing using a NMF (L2-VR) for a cloudless toy model.
SOT for time-varying geometry with a full Bayesian modeling (dynamic SOT) based on Kawahara and Masuda (2020). It also includes codes for the Bayesian version of the static SOT.
Figure (Click): Dynamic map using the real light curve (PC1) of Earth by Deep Space Climate Observatory.
SOT-Sparse uses L1 and Total Squared Variation (TSV).
- Spare (external)
The algorithm is based on Aizawa, Kawahara, Fan, ApJ, 896, 22 (2020).
Spin-Orbit Unmixing (SOU) is a unified retrieval model for spectral unmixing and spin-orbit tomography.
Spin-Orbit Unmixing using the non-negative matrix factorization (NMF) and L2 and volume regularization (SOU-NMF) based on Kawahara, ApJ, 894, 58 (2020).
Figure: The recovered composite map of the real light curve of Earth by Deep Space Climate Observatory using SOU-NMF.
- exomap, Kuwata, H.K., Aizawa et al. to be submitted.
The orientation of the spin axis can be inferred from frequency modulation (FM) of the light curve.
- fm/rottheory.py the modulation factor. It can reproduce Figure 2 in Kawahara (2016).
- juwvid Code for the Wigner-Ville analysis written in Julia-0.6.
The algorithm is based on Kawahara (2016). See also Nakagawa et al. (2020).
- ReflectDirect Python suite for analysis of planet reflected light by Joel Schwartz et al.
- exocartographer Bayesian framework of a 2D mapping and obliquity measurement by Ben Farr et al.
- EARL Spherical harmonics decomposition of reflected light by Hal Haggard et al.
- samurai Rotational Unmixing by Lustig-Yaeger et al.
- starry/starrynight Tools for mapping planets and stars by Rodrigo Luger et al and Rodrigo Luger et al.
- neural_exocartography mapping with learned denoiser (dnn) by Asensio-Ramos and Palle
- bluedot A pale blue dot simulater by Hajime Kawahara (beta version).