Orateur
Description
Large photometric surveys provide sparse multi-band lightcurves for millions of Solar System objects (SSOs),
offering an opportunity to jointly constrain their physical and compositional properties. However, current phase func-
tion models do not account for rotational variability, limiting their ability to retrieve accurate parameters. Similarly,
methods that recover shape and rotational parameters remain computationally expensive, making the extraction of
such properties prohibitive at scale.
We aim to develop a model capable of simultaneously retrieving the absolute magnitude, phase parameters, spin
state, and shape proportions of SSOs from sparse photometric data, while remaining efficient for large datasets.
We introduce the Shape, Orientation and Colors Combined Algorithm (SOCCA), which extends the HG1 G2
formalism by incorporating the projected surface of a rotating triaxial ellipsoid. The model jointly fits multi-band pho-
tometry, and includes a dedicated treatment of rotational period determination. We validate the method on simulated
LSST-like observations and on a real lightcurve.
SOCCA significantly improves the fit to photometric data, reducing the mean residuals to half, compared to
previous models. It retrieves the absolute magnitude with a scatter about three times smaller than existing approaches,
and improves the determination of phase parameters by a similar factor. The inclusion of shape and rotation increases
the number of valid solutions by ∼10-20% per filter, leading to an overall success rate of 50%.
By combining phase, shape, and rotational information in a single model, SOCCA provides a more complete
physical description of SSOs from sparse photometry. Its performance and scalability make it well suited for current
and upcoming large surveys such as the Zwicky Transient Facility (ZTF) and the recently launched Legacy Survey of
Space and Time (LSST).