Browsing by Subject "asteroids: general"

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  • Martikainen, J.; Muinonen, K.; Penttilä, A.; Cellino, A.; Wang, X. -B. (2021)
    Aims. We perform light curve inversion for 491 asteroids to retrieve phase curve parameters, rotation periods, pole longitudes and latitudes, and convex and triaxial ellipsoid shapes by using the sparse photometric observations from Gaia Data Release 2 and the dense ground-based observations from the DAMIT database. We develop a method for the derivation of reference absolute magnitudes and phase curves from the Gaia data, allowing for comparative studies involving hundreds of asteroids.Methods. For both general convex shapes and ellipsoid shapes, we computed least-squares solutions using either the Levenberg-Marquardt optimization algorithm or the Nelder-Mead downhill simplex method. Virtual observations were generated by adding Gaussian random errors to the observations, and, later on, a Markov chain Monte Carlo method was applied to sample the spin, shape, and scattering parameters. Absolute magnitude and phase curve retrieval was developed for the reference geometry of equatorial illumination and observations based on model magnitudes averaged over rotational phase.Results. The derived photometric slope values showed wide variations within each assumed Tholen class. The computed Gaia G-band absolute magnitudes matched notably well with the V-band absolute magnitudes retrieved from the Jet Propulsion Laboratory Small-Body Database. Finally, the reference phase curves were well fitted with the H, G(1), G(2) phase function. The resulting G(1), G(2) distribution differed, in an intriguing way, from the G(1), G(2) distribution that is based on the phase curves corresponding to light curve brightness maxima.
  • Muinonen, K.; Torppa, J.; Wang, X-B; Cellino, A.; Penttilä, A. (2020)
    Context. We assess statistical inversion of asteroid rotation periods, pole orientations, shapes, and phase curve parameters from photometric 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. We utilized these least-squares solutions to construct a proposal probability density for MCMC sampling. For inverse methods involving triaxial ellipsoids, we update the uncertainty model for the observations.Results. We demonstrate the utilization of the inverse methods for three asteroids with Gaia photometry from Data Release 2: (21) Lutetia, (26) Proserpina, and (585) Bilkis. First, we validated the convex inverse methods using the combined ground-based and Gaia data for Lutetia, arriving at rotation and shape models in agreement with those derived with the help of Rosetta space mission data. Second, we applied the convex inverse methods to Proserpina and Bilkis, illustrating the potential of the Gaia photometry for setting constraints on asteroid light scattering as a function of the phase angle (the Sun-object-observer angle). Third, with the help of triaxial ellipsoid inversion as applied to Gaia photometry only, we provide additional proof that the absolute Gaia photometry alone can yield meaningful photometric slope parameters. Fourth, for (585) Bilkis, we report, with 1-sigma uncertainties, a refined rotation period of (8.5750559 0.0000026) h, pole longitude of 320.6 degrees +/- 1.2 degrees, pole latitude of - 25.6 degrees +/- 1.7 degrees, and the first shape model and its uncertainties from convex inversion.Conclusions. We conclude that the inverse methods provide realistic uncertainty estimators for the lightcurve inversion problem and that the Gaia photometry can provide an asteroid taxonomy based on the phase curves.
  • Siltala, L.; Granvik, M. (2020)
    Context. The bulk density of an asteroid informs us about its interior structure and composition. To constrain the bulk density, one needs an estimated mass of the asteroid. The mass is estimated by analyzing an asteroid's gravitational interaction with another object, such as another asteroid during a close encounter. An estimate for the mass has typically been obtained with linearized least-squares methods, despite the fact that this family of methods is not able to properly describe non-Gaussian parameter distributions. In addition, the uncertainties reported for asteroid masses in the literature are sometimes inconsistent with each other and are suspected to be unrealistically low.Aims. We aim to present a Markov-chain Monte Carlo (MCMC) algorithm for the asteroid mass estimation problem based on asteroid-asteroid close encounters. We verify that our algorithm works correctly by applying it to synthetic data sets. We use astrometry available through the Minor Planet Center to estimate masses for a select few example cases and compare our results with results reported in the literature.Methods. Our mass-estimation method is based on the robust adaptive Metropolis algorithm that has been implemented into the OpenOrb asteroid orbit computation software. Our method has the built-in capability to analyze multiple perturbing asteroids and test asteroids simultaneously.Results. We find that our mass estimates for the synthetic data sets are fully consistent with the ground truth. The nominal masses for real example cases typically agree with the literature but tend to have greater uncertainties than what is reported in recent literature. Possible reasons for this include different astrometric data sets and weights, different test asteroids, different force models or different algorithms. For (16) Psyche, the target of NASA's Psyche mission, our maximum likelihood mass is approximately 55% of what is reported in the literature. Such a low mass would imply that the bulk density is significantly lower than previously expected and thus disagrees with the theory of (16) Psyche being the metallic core of a protoplanet. We do, however, note that masses reported in recent literature remain within our 3-sigma limits.Results. The new MCMC mass-estimation algorithm performs as expected, but a rigorous comparison with results from a least-squares algorithm with the exact same data set remains to be done. The matters of uncertainties in comparison with other algorithms and correlations of observations also warrant further investigation.
  • Penttilä, Antti; Hietala, Hilppa; Muinonen, Karri (2021)
    Aims. We explore the performance of neural networks in automatically classifying asteroids into their taxonomic spectral classes. We particularly focus on what the methodology could offer the ESA Gaia mission.Methods. We constructed an asteroid dataset that can be limited to simulating Gaia samples. The samples were fed into a custom-designed neural network that learns how to predict the samples' spectral classes and produces the success rate of the predictions. The performance of the neural network is also evaluated using three real preliminary Gaia asteroid spectra.Results. The overall results show that the neural network can identify taxonomic classes of asteroids in a robust manner. The success in classification is evaluated for spectra from the nominal 0.45-2.45 mu m wavelength range used in the Bus-DeMeo taxonomy, and from a limited range of 0.45-1.05 mu m following the joint wavelength range of Gaia observations and the Bus-DeMeo taxonomic system.Conclusions. The obtained results indicate that using neural networks to execute automated classification is an appealing solution for maintaining asteroid taxonomies, especially as the size of the available datasets grows larger with missions like Gaia.
  • Pöntinen, M.; Granvik, M.; Nucita, A. A.; Conversi, L.; Altieri, B.; Auricchio, N.; Bodendorf, C.; Bonino, D.; Brescia, M.; Capobianco, V.; Carretero, J.; Carry, B.; Castellano, M.; Cledassou, R.; Congedo, G.; Corcione, L.; Cropper, M.; Dusini, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Garilli, B.; Grupp, F.; Hormuth, F.; Israel, H.; Jahnke, K.; Kermiche, S.; Kitching, T.; Kohley, R.; Kubik, B.; Kunz, M.; Laureijs, R.; Lilje, P. B.; Lloro, I.; Maiorano, E.; Marggraf, O.; Massey, R.; Meneghetti, M.; Meylan, G.; Moscardini, L.; Padilla, C.; Paltani, S.; Pasian, F.; Pires, S.; Polenta, G.; Raison, F.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Schneider, P.; Secroun, A.; Serrano, S.; Sirri, G.; Tereno, I.; Toledo-Moreo, R.; Valenziano, L.; Wetzstein, M.; Zoubian, J. (2020)
    Context. The ESA Euclid space telescope could observe up to 150 000 asteroids as a side product of its primary cosmological mission. Asteroids appear as trailed sources, that is streaks, in the images. Owing to the survey area of 15 000 square degrees and the number of sources, automated methods have to be used to find them. Euclid is equipped with a visible camera, VIS (VISual imager), and a near-infrared camera, NISP (Near-Infrared Spectrometer and Photometer), with three filters.Aims. We aim to develop a pipeline to detect fast-moving objects in Euclid images, with both high completeness and high purity.Methods. We tested the StreakDet software to find asteroids from simulated Euclid images. We optimized the parameters of StreakDet to maximize completeness, and developed a post-processing algorithm to improve the purity of the sample of detected sources by removing false-positive detections.Results.StreakDet finds 96.9% of the synthetic asteroid streaks with apparent magnitudes brighter than 23rd magnitude and streak lengths longer than 15 pixels (10 arcsec h(-1)), but this comes at the cost of finding a high number of false positives. The number of false positives can be radically reduced with multi-streak analysis, which utilizes all four dithers obtained by Euclid.Conclusions.StreakDet is a good tool for identifying asteroids in Euclid images, but there is still room for improvement, in particular, for finding short (less than 13 pixels, corresponding to 8 arcsec h(-1)) and/or faint streaks (fainter than the apparent magnitude of 23).
  • Shevchenko, Vasilij G.; Belskaya, Irina N.; Mikhalchenko, Olga I.; Muinonen, Karri; Penttilä, Antti; Gritsevich, Maria; Shkuratov, Yuriy G.; Slyusarev, Ivan G.; Videen, Gorden (2019)
    The values of the phase integral q were determined for asteroids using a numerical integration of the brightness phase functions over a wide phase-angle range and the relations between q and the G parameter of the HG function and q and the G(1), G(2) parameters of the HG(1)G(2) function. The phase-integral values for asteroids of different geometric albedo range from 0.34 to 0.54 with an average value of 0.44. These values can be used for the determination of the Bond albedo of asteroids. Estimates for the phase-integral values using the G(1) and G(2) parameters are in very good agreement with the available observational data. We recommend using the HG(1)G(2) function for the determination of the phase integral. Comparison of the phase integrals of asteroids and planetary satellites shows that asteroids have systematically lower values of q.
  • Carry, B.; Thuillot, W.; Spoto, F.; David, P.; Berthier, J.; Tanga, P.; Mignard, F.; Bouquillon, S.; Mendez, R. A.; Rivet, J. -P.; Le Van Suu, A.; Dell'Oro, A.; Fedorets, G.; Frezouls, B.; Granvik, M.; Guiraud, J.; Muinonen, K.; Panem, C.; Pauwels, T.; Roux, W.; Walmsley, G.; Petit, J. -M.; Abe, L.; Ayvazian, V.; Baillié, K.; Baransky, A.; Bendjoya, P.; Dennefeld, M.; Desmars, J.; Eggl, S.; Godunova, V.; Hestroffer, D.; Inasaridze, R.; Kashuba, V.; Krugly, Y. N.; Molotov, I. E.; Robert, V.; Simon, A.; Sokolov, I.; Souami, D.; Tarady, V.; Taris, F.; Troianskyi, V.; Vasylenko, V.; Vernet, D. (2021)
    Context. Since July 2014, the Gaia mission of the European Space Agency has been surveying the entire sky down to magnitude 20.7 in the visible. In addition to the millions of daily observations of stars, thousands of Solar System objects (SSOs) are observed. By comparing their positions, as measured by Gaia, to those of known objects, a daily processing pipeline filters known objects from potential discoveries. However, owing to Gaia's specific observing mode, which follows a predetermined scanning law designed for stars as "fixed" objects on the celestial sphere, potential newly discovered moving objects are characterized by very few observations, which are acquired over a limited time. Furthermore, these objects cannot be specifically targeted by Gaia itself after their first detection. This aspect was recognized early on in the design of the Gaia data processing.Aims. A daily processing pipeline dedicated to these candidate discoveries was set up to release calls for observations to a network of ground-based telescopes. Their aim is to acquire follow-up astrometry and to characterize these objects.Methods. From the astrometry measured by Gaia, preliminary orbital solutions are determined, allowing us to predict the position of these potentially newly discovered objects in the sky while accounting for the large parallax between Gaia and the Earth (separated by 0.01 au). A specific task within the Gaia Data Processing and Analysis Consortium has been responsible for the distribution of requests for follow-up observations of potential Gaia SSO discoveries. Since late 2016, these calls for observations (nicknamed "alerts") have been published via a Web interface with a quasi-daily frequency, together with observing guides, which is freely available to anyone worldwide.Results. Between November 2016 and the end of the first year of the extended mission (July 2020), over 1700 alerts were published, leading to the successful recovery of more than 200 objects. Among them, six have a provisional designation assigned with the Gaia observations; the others were previously known objects with poorly characterized orbits, precluding identification at the time of Gaia observations. There is a clear trend for objects with a high inclination to be unidentified, revealing a clear bias in the current census of SSOs against high-inclination populations.
  • Escobar-Cerezo, J.; Penttilä, Antti; Kohout, T.; Munoz, O.; Moreno, F.; Muinonen, K. (2018)
    Lunar soil spectra differ from pulverized lunar rocks spectra by reddening and darkening effects, and shallower absorption bands. These effects have been described in the past as a consequence of space weathering. In this work, we focus on the effects of nanophase iron (npFe(0)) inclusions on the experimental reflectance spectra of lunar regolith particles. The reflectance spectra are computed using SIRIS3, a code that combines ray optics with radiative-transfer modeling to simulate light scattering by different types of scatterers. The imaginary part of the refractive index as a function of wavelength of immature lunar soil is derived by comparison with the measured spectra of the corresponding material. Furthermore, the effect of adding nanophase iron inclusions on the reflectance spectra is studied. The computed spectra qualitatively reproduce the observed effects of space weathered lunar regolith.