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Register a new userProbabilistic modeling of asteroid diameters from Gaia DR2 errors
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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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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.
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.
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.
The Gaia mission started its regular observing program in the summer of 2014, and since then it is regularly obtaining observations of asteroids. This paper draws the outline of the data processing for Solar System objects, and in particular on the daily short-term processing, from the on-board data acquisition to the ground-based processing. We illustrate the tools developed to compute predictions of asteroid observations, we discuss the procedures implemented by the daily processing, and we illustrate some tests and validations of the processing of the asteroid observations. Our findings are overall consistent with the expectations concerning the performances of Gaia and the effectiveness of the developed software for data reduction.
Gaias Early Third Data Release (EDR3) does not contain new radial velocities because these will be published in Gaias full third data release (DR3), expected in the first half of 2022. To maximise the usefulness of EDR3, Gaias second data release (DR2) sources (with radial velocities) are matched to EDR3 sources to allow their DR2 radial velocities to also be included in EDR3. This presents two considerations: (i) arXiv:1901.10460 (hereafter B19) published a list of 70,365 sources with potentially contaminated DR2 radial velocities; and (ii) EDR3 is based on a new astrometric solution and a new source list, which means sources in DR2 may not be in EDR3. EDR3 contains 7,209,831 sources with a DR2 radial velocity, which is 99.8% of sources with a radial velocity in DR2. 14,800 radial velocities from DR2 are not propagated to any EDR3 sources because (i) 3871 from the B19 list are found to either not have an unpublished, preliminary DR3 radial velocity or it differs significantly from its DR2 value, and 5 high-velocity stars not in the B19 list are confirmed to have contaminated radial velocities; and (ii) 10,924 DR2 sources could not be satisfactorily matched to any EDR3 sources, so their DR2 radial velocities are also missing from EDR3. The reliability of radial velocities in EDR3 has improved compared to DR2 because the update removes a small fraction of erroneous radial velocities (0.05% of DR2 radial velocities and 5.5% of the B19 list). Lessons learnt from EDR3 (e.g. bright star contamination) will improve the radial velocities in future Gaia data releases. The main reason for radial velocities from DR2 not propagating to EDR3 is not related to DR2 radial velocity quality. It is because the DR2 astrometry is based on one component of close binary pairs, while EDR3 astrometry is based on the other component, which prevents these sources from being unambiguously matched. (Abridged)
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