New and updated convex shape models of asteroids based on optical data from a large collaboration network

Hanuš, Josef; Ďurech, Josef; Oszkiewicz, D.; Behrend, R.; Carry, B.; Delbò, M.; Adam, O.; Afonina, V. V.; Anquetin, R.; Antonini, P.; Arnold, L.; Audejean, M.; Aurard, P.; Bachschmidt, M.; Baduel, B.; Barbotin, E.; Barroy, P.; Baudouin, P.; Berard, L.; Berger, Noam; Bernasconi, L.; Bosch, J. G.; Bouley, Sylvain; Bozhinova, Inna; Brinsfield, James W.; Brunetto, L.; Canaud, G.; Caron, J.; Carrier, F.; Casalnuovo, Giovanni Battista; Casulli, Silvano; Cerda, Marcos; Chalamet, L.; Charbonnel, S.; Chinaglia, Benedetto; Cikota, Aleksandar; Colas, F.; Coliac, J.-F.; Collet, Agwilh; Coloma, J. M.; Conjat, M.; Conseil, E.; Costa, R.; Crippa, R.; Cristofanelli, M.; Damerdji, Y.; Debackère, A.; Decock, Alice; Déhais, Q.; Déléage, T.; Delmelle, S.; Demeautis, C.; Dróżdż, M.; Dubos, G.; Dulcamara, T.; Dumont, M.; Durkee, Russell I.; Dymock, R.; Valle, A. Escalante del; Esseiva, N.; Esseiva, R.; Esteban, M.; Fauchez, Thomas; Fauerbach, M.; Fauvaud, M.; Fauvaud, S.; Forné, E.; Fournel, C.; Fradet, D.; Garlitz, J.; Gerteis, O.; Gillier, C.; Gillon, M.; Giraud, R.; Godard, J.-P.; Gonçalves, R.; Hamanowa, Hiroko; Hamanowa, Hiromi; Hay, K. L.; Hellmich, S.; Heterier, S.; Higgins, David; Hirsch, R.; Hodosán, Gabriella; Hren, M.; Hygate, Alexander P. S.; Innocent, N.; Jacquinot, H.; Jawahar, S.; Jehin, Emmanuël; Jerosimic, L.; Klotz, A.; Koff, W.; Korlevic, P.; Kosturkiewicz, E.; Krafft, P.; Krugly, Yu. N.; Kugel, F.; Labrevoir, O.; Lecacheux, J.; Lehký, M.; Leroy, A.; Lesquerbault, B.; Lopez-Gonzales, M. J.; Lutz, Mikaël; Mallecot, B.; Manfroid, Jean; Manzini, F.; Marciniak, A.; Martin, Ann M.; Modave, B.; Montaigut, R.; Montier, J.; Morelle, Etienne; Morton, Bruce; Mottola, S.; Naves, R.; Nomen, J.; Oey, Julian; Ogłoza, W.; Paiella, M.; Pallares, H.; Peyrot, A.; Pilcher, Frederick; Pirenne, Jacques; Piron, Pierre; Polińska, M.; Polotto, M.; Poncy, R.; Previt, J. P.; Reignier, F.; Renauld, D.; Ricci, Davide; Richard, F.; Rinner, C.; Risoldi, V.; Robilliard, Denis; Romeuf, D.; Rousseau, Guy; Roy, R.; Ruthroff, John C.; Salom, P.; Salvador, Luigi; Sánchez, Salvador; Santana-Ros, T.; Scholz, Alexander; Séné, G.; Skiff, B. A.; Sobkowiak, K.; Sogorb, P.; Soldán, F.; Spiridakis, A.; Splanska, E.; Sposetti, S.; Starkey, D.; Stephens, Robert D.; Stiepen, A.; Stoss, R.; Strajnic, J.; Teng, J. P.; Tumolo, G.; Vagnozzi, A.; Vanoutryve, B.; Vugnon, J.-M.; Warner, Brian D.; Waucomont, M.; Wertz, O.; Winiarski, M.; Wolf, M.

doi:10.1051/0004-6361/201527441

Cited by 60 publications

(33 citation statements)

References 62 publications

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“…(526) Jena The model published by Hanuš et al (2016) lists an incorrect rotation period of 9.52 h because the best-fit period was searched only near the value of 9.474 h reported by Barucci et al (1994). The new model with a period 11.88 h is clearly better and fits well not only the light curves of Barucci et al (1994) but also sparse USNO and Gaia photometry.…”

Section: Comparison With Damit Modelsmentioning

confidence: 97%

Inversion of asteroid photometry from Gaia DR2 and the Lowell Observatory photometric database

Ďurech

Hanuš

Vanco

2019

A&A

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Context. 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. Aims. 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. Methods. 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.Results. 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. Conclusions. 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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Section: Comparison With Damit Modelsmentioning

confidence: 97%

Inversion of asteroid photometry from Gaia DR2 and the Lowell Observatory photometric database

Ďurech

Hanuš

Vanco

2019

A&A

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“…This general rule certainly works for large main belt asteroids, that is, the Gaia mass targets. We do not consider the errors that are introduced by the pole orientation uncertainties or the shapes (see Hanuš et al 2016 andDudziński 2019); therefore, our TPM error bars are lower estimates of the true error bars. The previously mentioned general rule or expectation is based on the fact that the flux is proportional to the square of the projected area, so fitting a highquality shape and spin model to fluxes with 10% absolute error bars should produce a ∼ 5% accurate size.…”

Section: Thermophysical Modeling (Tpm)mentioning

confidence: 99%

“…Thanks to the observing campaign that was conducted in the framework of the SBNAF project and the GaiaGOSA observers, we were able to determine the shape and spin state. Without the new data, the previous set of viewing geometries would have been limited to only 1/3 of the Myrrha orbit, and the earlier model by Hanuš et al (2016) was constructed on dense lightcurves supplemented with sparse data. As a consequence, the previous model looks somewhat angular (cf.…”

Section: (381) Myrrhamentioning

confidence: 99%

Physical parameters of selected Gaia mass asteroids

Podlewska-Gaca

Marciniak

Alí-Lagoa

et al. 2020

A&A

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Context. Thanks to the Gaia mission, it will be possible to determine the masses of approximately hundreds of large main belt asteroids with very good precision. We currently have diameter estimates for all of them that can be used to compute their volume and hence their density. However, some of those diameters are still based on simple thermal models, which can occasionally lead to volume uncertainties as high as 20-30%. Aims. The aim of this paper is to determine the 3D shape models and compute the volumes for 13 main belt asteroids that were selected from those targets for which Gaia will provide the mass with an accuracy of better than 10%. Methods. We used the genetic Shaping Asteroids with Genetic Evolution (SAGE) algorithm to fit disk-integrated, dense photometric lightcurves and obtain detailed asteroid shape models. These models were scaled by fitting them to available stellar occultation and/or thermal infrared observations. Results. We determine the spin and shape models for 13 main belt asteroids using the SAGE algorithm. Occultation fitting enables us to confirm main shape features and the spin state, while thermophysical modeling leads to more precise diameters as well as estimates of thermal inertia values. Conclusions. We calculated the volume of our sample of main-belt asteroids for which the Gaia satellite will provide precise mass determinations. From our volumes, it will then be possible to more accurately compute the bulk density, which is a fundamental physical property needed to understand the formation and evolution processes of small solar system bodies.

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“…New models can be derived not only by collecting more new observations, but also just by processing archival photometric observation of large surveys. This datamining approach was used by Hanuš et al (2011Hanuš et al ( , 2013bHanuš et al ( , 2016Hanuš et al ( ) andĎurech et al (2016, for example.…”

Section: Introductionmentioning

confidence: 99%

Asteroid models reconstructed from the Lowell Photometric Database and WISE data

Ďurech

Hanuš

Alí-Lagoa

2018

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Context. 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. Aims. 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 NASA's WISE satellite. Methods. 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. Results. 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. Apart from updating the statistics for the dependence of the distribution on asteroid size, we revealed a significant discrepancy between the number of prograde and retrograde rotators for asteroids smaller than about 10 km. Conclusions. Combining optical photometry with thermal infrared light curves is an efficient approach to obtaining new physical models of asteroids. The amount of asteroid photometry is continuously growing and joint inversion of data from different surveys could lead to thousands of new models in the near future.

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New and updated convex shape models of asteroids based on optical data from a large collaboration network

Cited by 60 publications

References 62 publications

Inversion of asteroid photometry from Gaia DR2 and the Lowell Observatory photometric database

Inversion of asteroid photometry from Gaia DR2 and the Lowell Observatory photometric database

Physical parameters of selected Gaia mass asteroids

Asteroid models reconstructed from the Lowell Photometric Database and WISE data

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