A&A 642, A138 (2020) Astronomy https://doi.org/10.1051/0004-6361/202038036 & © K. Muinonen et al. 2020 Astrophysics Asteroid lightcurve inversion with Bayesian inference K. Muinonen1,2, J. Torppa3, X.-B. Wang4,5, A. Cellino6, and A. Penttilä1 1 Department of Physics, University of Helsinki, Gustaf Hällströmin katu 2a, PO Box 64, 00014 U. Helsinki, Finland e-mail:
[email protected] 2 Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, 02430 Masala, Finland 3 Space Systems Finland, Kappelitie 6, 02200 Espoo, Finland 4 Yunnan Observatories, CAS, PO Box 110, Kunming 650216, PR China 5 School of Astronomy and Space science, University of Chinese Academy of Sciences, Beijing 100049, PR China 6 INAF, Osservatorio Astrofisico di Torino, Strada Osservatorio 20, 10025 Pino Torinese (TO), Italy Received 27 March 2020 / Accepted 9 August 2020 ABSTRACT Context. We assess statistical inversion of asteroid rotation periods, pole orientations, shapes, and phase curve parameters from pho- tometric lightcurve observations, here sparse data from the ESA Gaia space mission (Data Release 2) or dense and sparse data from ground-based observing programs. Aims. Assuming general convex shapes, we develop inverse methods for characterizing the Bayesian a posteriori probability density of the parameters (unknowns). We consider both random and systematic uncertainties (errors) in the observations, and assign weights to the observations with the help of Bayesian a priori probability densities. Methods. For general convex shapes comprising large numbers of parameters, we developed a Markov-chain Monte Carlo sampler (MCMC) with a novel proposal probability density function based on the simulation of virtual observations giving rise to virtual least-squares solutions.