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Register a new userInversion of asteroid photometry from Gaia DR2 and the Lowell Observatory photometric database
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Rotation properties (spin-axis direction and rotation period) and coarse shape models of asteroids can be reconstructed from their disk-integrated brightness when measured from various viewing geometries. These physical properties are essential for creating a global picture of structure and dynamical evolution of the main belt. The number of shape and spin models can be increased not only when new data are available, but also by combining independent data sets and inverting them together. Our aim was to derive new asteroid models by processing readily available photometry. We used asteroid photometry compiled in the Lowell Observatory photometry database with photometry from the Gaia Data Release 2. Both data sources are available for about 5400 asteroids. In the framework of the Asteroids@home distributed computing project, we applied the light curve inversion method to each asteroid to find its convex shape model and spin state that fits the observed photometry. Due to the limited number of Gaia DR2 data points and poor photometric accuracy of Lowell data, we were able to derive unique models for only ~1100 asteroids. Nevertheless, 762 of these are new models that significantly enlarge the current database of about 1600 asteroid models. Our results demonstrate the importance of a combined approach to inversion of asteroid photometry. While our models in general agree with those obtained by separate inversion of Lowell and Gaia data, the combined inversion is more robust, model parameters are more constrained, and unique models can be reconstructed in many cases when individual data sets alone are not sufficient.
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We use the lightcurve inversion method to derive new shape models and spin states of asteroids from the sparse-in-time photometry compiled in the Lowell Photometric Database. To speed up the time-consuming process of scanning the period parameter space through the use of convex shape models, we use the distributed computing project Asteroids@home, running on the Berkeley Open Infrastructure for Network Computing (BOINC) platform. This way, the period-search interval is divided into hundreds of smaller intervals. These intervals are scanned separately by different volunteers and then joined together. We also use an alternative, faster, approach when searching the best-fit period by using a model of triaxial ellipsoid. By this, we can independently confirm periods found with convex models and also find rotation periods for some of those asteroids for which the convex-model approach gives too many solutions. From the analysis of Lowell photometric data of the first 100,000 numbered asteroids, we derived 328 new models. This almost doubles the number of available models. We tested the reliability of our results by comparing models that were derived from purely Lowell data with those based on dense lightcurves, and we found that the rate of false-positive solutions is very low. We also present updated plots of the distribution of spin obliquities and pole ecliptic longitudes that confirm previous findings about a non-uniform distribution of spin axes. However, the models reconstructed from noisy sparse data are heavily biased towards more elongated bodies with high lightcurve amplitudes.
Information about the spin state of asteroids is important for our understanding of the dynamical processes affecting them. However, spin properties of asteroids are known for only a small fraction of the whole population. To enlarge the sample of asteroids with a known rotation state and basic shape properties, we combined sparse-in-time photometry from the Lowell Observatory Database with flux measurements from NASAs WISE satellite. We applied the light curve inversion method to the combined data. The thermal infrared data from WISE were treated as reflected light because the shapes of thermal and visual light curves are similar enough for our purposes. While sparse data cover a wide range of geometries over many years, WISE data typically cover an interval of tens of hours, which is comparable to the typical rotation period of asteroids. The search for best-fitting models was done in the framework of the Asteroids@home distributed computing project. By processing the data for almost 75,000 asteroids, we derived unique shape models for about 900 of them. Some of them were already available in the DAMIT database and served us as a consistency check of our approach. In total, we derived new models for 662 asteroids, which significantly increased the total number of asteroids for which their rotation state and shape are known. For another 789 asteroids, we were able to determine their sidereal rotation period and estimate the ecliptic latitude of the spin axis direction. We studied the distribution of spins in the asteroid population. We revealed a significant discrepancy between the number of prograde and retrograde rotators for asteroids smaller than about 10 km. Combining optical photometry with thermal infrared light curves is an efficient approach to obtaining new physical models of asteroids.
In addition to stellar data, Gaia Data Release 2 (DR2) also contains accurate astrometry and photometry of about 14,000 asteroids covering 22 months of observations. We used Gaia asteroid photometry to reconstruct rotation periods, spin axis directions, and the coarse shapes of a subset of asteroids with enough observations. One of our aims was to test the reliability of the models with respect to the number of data points and to check the consistency of these models with independent data. Another aim was to produce new asteroid models to enlarge the sample of asteroids with known spin and shape. We used the lightcurve inversion method to scan the period and pole parameter space to create final shape models that best reproduce the observed data. To search for the sidereal rotation period, we also used a simpler model of a geometrically scattering triaxial ellipsoid. By processing about 5400 asteroids with at least ten observations in DR2, we derived models for 173 asteroids, 129 of which are new. Models of the remaining asteroids were already known from the inversion of independent data, and we used them for verification and error estimation. We also compared the formally best rotation periods based on Gaia data with those derived from dense lightcurves. We show that a correct rotation period can be determined even when the number of observations $N$ is less than 20, but the rate of false solutions is high. For $N > 30$, the solution of the inverse problem is often successful and the parameters are likely to be correct in most cases. These results are very promising because the final Gaia catalogue should contain photometry for hundreds of thousands of asteroids, typically with several tens of data points per object, which should be sufficient for reliable spin reconstruction.
Gaia Data Release 2 includes observational data for 14,099 pre-selected asteroids. From the sparsely sampled G band photometry, we derive lower-limit lightcurve amplitudes for 11,665 main belt asteroids in order to provide constraints on the distribution of shapes in the asteroid main belt. Assuming a triaxial shape model for each asteroid, defined through the axial aspect ratios a > b and b=c, we find an average b/a=0.80+-0.04 for the ensemble, which is in agreement with previous results. By combining the Gaia data with asteroid properties from the literature, we investigate possible correlations of the aspect ratio with size, semi-major axis, geometric albedo, and intrinsic color. Based on our model simulations, we find that main belt asteroids greater than 50 km in diameter on average have higher b/a aspect ratios (are rounder) than smaller asteroids. We furthermore find significant differences in the shape distribution of main belt asteroids as a function of the other properties that do not affect the average aspect ratios. We conclude that a more detailed investigation of shape distribution correlations requires a larger data sample than is provided in Gaia Data Release 2.
The Gaia Data Release 2 provides precise astrometry for nearly 1.5 billion sources across the entire sky, including several thousand asteroids. In this work, we provide evidence that reasonably large asteroids (diameter $>$ 20 km) have high correlations with Gaia relative flux uncertainties and systematic right ascension errors. We further capture these correlations using a logistic Bayesian additive regression tree model. We compile a small list of probable large asteroids that can be targeted for direct diameter measurements and shape reconstruction.
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