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A&A
Volume 619, November 2018
Article Number L3
Number of page(s) 10
Section Letters to the Editor
DOI https://doi.org/10.1051/0004-6361/201834091
Published online 09 November 2018

© ESO 2018

1. Introduction

Asteroid (16) Psyche, the target of the NASA Discovery mission Psyche (Elkins-Tanton et al. 2017), is one of the very few main-belt asteroids that exhibits a relatively high radar albedo (0.42 ± 0.1, Shepard et al. 2010, 2015) and shallow phase-polarization minimum (Dollfus & Geake 1977; Dollfus et al. 1979), which imply that its surface/subsurface is metal-rich. On such a basis, it has been defined as a metallic world (Elkins-Tanton et al. 2017) and is considered one of the few main-belt bodies that could be an exposed planetary metallic core and that could thus be related to iron meteorites. This association is however challenged by both its near- and mid-infrared spectral properties and its density.

Spectroscopic observations in the near-infrared range have revealed the presence of (i) a weak (∼1%) 0.9 μm absorption band suggesting the presence of orthopyroxene on its surface (e.g., Hardersen et al. 2005; Ockert-Bell et al. 2008; Sanchez et al. 2017) and (ii) a 3 μm absorption feature suggesting the presence of hydrated silicates on its surface (Takir et al. 2017). Spectroscopic observations with the Spitzer Space Telescope in the thermal infrared have provided additional evidence for the presence of fine-grained silicates at the surface of Psyche but have also detected the presence of a metallic bedrock (Landsman et al. 2018) consistent with earlier radar observations (Shepard et al. 2010). Finally, its thermal inertia is among the highest for an asteroid of this size (Matter et al. 2013). At this stage, one cannot exclude that some of the silicates (especially the hydrated ones) on the surface of the asteroid could have an exogenous origin (Avdellidou et al. 2018). We note that the presence of exogenous dust has already been reported at the surfaces of a number of large main-belt asteroids, including Vesta (Reddy et al. 2012) and Ceres (Vernazza et al. 2017).

Spectroscopic observations in the near-infrared range have revealed the presence of (i) a weak (∼1%) 0.9 μm absorption band suggesting the presence of orthopyroxene on its surface (e.g., Hardersen et al. 2005; Ockert-Bell et al. 2008; Sanchez et al. 2017) and (ii) a 3 μm absorption feature suggesting the presence of hydrated silicates on its surface (Takir et al. 2017). Spectroscopic observations with the Spitzer Space Telescope in the thermal infrared have provided additional evidence for the presence of fine-grained silicates at the surface of Psyche but have also detected the presence of a metallic bedrock (Landsman et al. 2018) consistent with earlier radar observations (Shepard et al. 2010). Finally, its thermal inertia is among the highest for an asteroid of this size (Matter et al. 2013). At this stage, one cannot exclude that some of the silicates (especially the hydrated ones) on the surface of the asteroid could have an exogenous origin (Avdellidou et al. 2018). We note that the presence of exogenous dust has already been reported at the surfaces of a number of large main-belt asteroids, including Vesta (Reddy et al. 2012) and Ceres (Vernazza et al. 2017).

What is more puzzling at this stage is the reported density for Psyche. Recent estimates of its density all fall in the 3.7–4.7 g cm−3 range (Hanus et al. 2017; Shepard et al. 2017; Drummond et al. 2018). Considering that large main-belt asteroids with a diameter greater than 200 km tend to have minimal macroporosity (≤10%, Carry 2012; Scheeres et al. 2015), one may expect this to be the case for Psyche (D ∼ 225 km), which would significantly complicate a direct association between this asteroid and iron meteorites.

In the present study, we present high-angular-resolution imaging observations of Psyche with ESO VLT/SPHERE/ZIMPOL that were performed as part of our large program (ID 199.C-0074; PI: P. Vernazza). We use these observations to (i) refine Psyche’s 3D shape model, and subsequently its density when combined with the most recent mass estimates, (ii) place for the first time constraints on the surface topography of the northern hemisphere of Psyche, and (iii) search for potential companions. Finally, we discuss what could be the most likely meteorite analog to Psyche.

2. Observations and data processing

We observed Psyche with VLT/SPHERE/ZIMPOL (Beuzit et al. 2008; Thalmann et al. 2008) at five different epochs, close to its opposition. Observational circumstances are listed in Table A.1. The data reduction was performed as described in Vernazza et al. (2018). Finally, the optimal angular resolution of each image was restored with the Mistral algorithm (Fusco et al. 2003; Mugnier et al. 2004).

Instead of using a stellar point-spread function (PSF) for the deconvolution, we used for the first time a parametric PSF with a Moffat shape (Moffat 1969). During our large program, deconvolution with the stellar PSF acquired just after the asteroid observation has sometimes led to unsatisfactory results. On some occasions, using stellar PSFs acquired on different nights improved the results.

We therefore tested deconvolution with PSFs modeled by a 2D Moffat function. Whereas the results of this Moffat-PSF deconvolution were similar to those obtained with the stellar PSF, the deconvolution with a Moffat PSF always converged toward satisfactory results (Fetick et al., in prep.). We therefore started systematically using a parametric PSF with a Moffat shape to deconvolve our images. The deconvolved images of asteroid Psyche are shown in Figs. 1 and A.1.

thumbnail Fig. 1.

First row: VLT/SPHERE/ZIMPOL images of Psyche obtained at five different epochs (ordered by rotation phase) and deconvolved with the Mistral algorithm and a parametric PSF with a Moffat shape (Sect. 2). Second row: corresponding shape model projections. Third row: corresponding projections using the shape model of Shepard et al. (2017). The arrow shows the direction of the rotation axis. Last row: images with contrast and size enhanced to highlight the albedo variegation. The two topographic regions that we consider as real features are also indicated.
Open with DEXTER

Lastly, for each epoch we produced an average image to increase the signal-to-noise ratio (S/N) and to perform a search for satellites (as we did for (41) Daphne, the first binary asteroid studied in our large program; see Carry et al. 2018).

3. Moon search around Psyche

We did not discover any companion around Psyche. As a subsequent step, we estimated the minimum diameter above which a moon would have been detected. Defining a threshold S/N at which the detection occurs is complex because 1) the diffracted light (generating background photon noise) is not symmetric around Psyche, 2) the instrumental speckle noise does not follow a normal law, and 3) the sample size is small (small number of pixels (Moffats-PSF) at any small separation with Psyche), thus preventing a robust statistical analysis.

To place an upper limit on the diameter of a companion, we simulated ten companions on a circle about on Airy disk away from the edge of the asteroid, where the photon noise from the diffracted light reaches its maximum value. The artificial moons were represented by normalized Airy disks multiplied by a constant flux to reach a local S/N of 1–3, and by the typical Strehl ratio of our observations of 0.7. The flux of each moon (Fm) was then adjusted to be at the detection limit in each of our images. As such, a moon detectable around Psyche should have the following minimum diameter (Dm):

(1)

where F16 is the integrated flux of Psyche and D16 is the surface-equivalent diameter of Psyche on the plane of the sky (that is 170 km). We found that any moon with a diameter Dm above 1460 ± 200 m at 150 km from the primary (or 0.2% × RHill, the Hill radius) would have been detected.

We also explored more remote orbits typical of large main-belt asteroids (at several primary radii; see Margot et al. 2015). At 100 pixels (about 2000 km) from Psyche, corresponding to 3% × R Hill where most of the satellites of 100+km asteroids have been found (Margot et al. 2015; Yang et al. 2016), the images are less affected by the diffracted light and we found that there should be no moon larger than Dm = 800 ± 200 m at these distances.

4. Size, shape and density

Recently, Shepard et al. (2017) derived a shape model of Psyche using range-Doppler imaging. The S/N of radar echoes was sufficient for identifying two crater-like depressions in the southern hemisphere, but the northern hemisphere remained unobserved and modeled with an ellipsoidal shape. Our SPHERE observations are highly complementary because they covered the northern hemisphere.

We used our All-Data Asteroid Modeling (ADAM) inversion technique (Viikinkoski & Kaasalainen 2014; Viikinkoski et al. 2015; Viikinkoski 2016; Hanus et al. 2017) to reconstruct the 3D shape model and the spin of Psyche. We utilized all available optical lightcurves (206), stellar occultations (2), and disk-resolved images (38). Our 25 VLT/SPHERE/ZIMPOL images were complemented by 11 Keck/NIRC2 and 2 VLT/NACO imaging epochs (Hanus et al. 2017; Shepard et al. 2017; Drummond et al. 2018), out of which 8 were taken under different sub-observer latitudes, complementary to our SPHERE images (Table A.1).

We also included two multiple-chord stellar occultations (Fig. A.2) recorded in 2010 and 2014 (Dunham et al. 2017). The 2014 occultation proved to be essential to constrain the size of the model along its rotation axis. Finally, we used a dataset of disk-integrated optical data consisting of 209 lightcurves in total: 118 lightcurves from van Houten-Groeneveld & van Houten (1958), Chang & Chang (1963), Tedesco & Taylor (1985), Taylor (1977), Tedesco et al. (1983), Lupishko et al. (1980, 1982), Zhou & Yang (1981), Zhou et al. (1982), Weidenschilling et al. (1987), Harris et al. (1999), Pfleiderer et al. (1987), Dotto et al. (1992), Weidenschilling et al. (1990), and Neely (1992), already used by Hanus et al. (2017), 3 lightcurves obtained by Warner (2016) in 2015, 85 lightcurves extracted from the SuperWASP image archive (Grice et al. 2017), and finally 3 lightcurves obtained in 2018 by Emmanuel Jehin in the Rc filter using the TRAPPIST South telescope.

The shape reconstruction was made more complex by discernible albedo variegation apparent both in the images and in the lightcurves. To model this phenomenon, we allowed each facet to have different relative brightness scaling parameters (i.e., to represent albedo variegation). Here, the albedo variegation was optimized simultaneously with the shape using both the lightcurves and imaging data, contrarily to Shepard et al. (2017) who used 114 lightcurves to fit albedo variegation to the fixed shape model. To avoid spurious spotty appearances, we defined a smoothing operator as a regularization term to discourage large deviations between neighboring facets. We chose, rather arbitrarily, ±30% as a reasonable interval for permitted variegation. The albedo distribution on the model is based mainly on lightcurves and is therefore not unique. However, both LSL (Kaasalainen et al. 2001) and Hapke (Hapke 1984) scattering laws resulted in almost identical albedo distributions.

We present in Fig. 1 the five SPHERE epochs, together with the corresponding projections of our shape model and of the radar-derived shape model. Our spin solution and volume-equivalent diameter D (226 ± 5 km) agree well with those reported in Shepard et al. (2017) and in several other studies (Table 1). The mass-deficit region (Shepard et al. 2017) is clearly visible in each SPHERE image, but its shape and size differ from the radar estimates. Furthermore, the projected area of Psyche on the plane of the sky is systematically underestimated by the radar model, as suggested by the differences in diameter along the three axes reported in Table 1. The polar region appears to be more flattened than suggested by the radar model. The albedo distribution (Fig. A.4) however closely matches the one presented by Shepard et al. (2017). The 3D shape model is depicted in Fig. A.3. One should keep in mind that only features visible and consistent in at least two images can be considered as genuine. If a feature is only seen in one image its plausibility depends on how reliable one assumes the post-processing of the image to be.

Table 1.

Physical parameters of Psyche derived in this study compared with previous works.

We then gathered 27 mass estimates and 15 diameter estimates of Psyche from the literature (Tables A.1 and A.2), which show significant scatter (Figs. A.5 and A.6). We therefore evaluated the reliability of each value and estimated a representative value of 24.1  ±  3.2)  ×  1018 kg following the procedure developed by Carry et al. (2012) or Vernazza et al. (2018). We used this value together with our new size estimate to compute Psyche’s bulk density of 3.99 ± 0.26 g cm−3.

5. Surface topography

Although our observations covered only the northern hemisphere, they reveal the presence of two peculiar units with low and high brightness (Fig. 1). We named these two units after the twin witches related to the Metamorphoses, the latin novel of Apuleius in which Psyche is a character.

Of the two units, the dark unit Meroe clearly appears as a crater. Using the same approach as the one described in Vernazza et al. (2018), we measured its diameter to be in the 80−100 km range. We subsequently estimated the width and depth of the depression present in the contour of the Panthia unit to be ∼90 km and ∼10 km, respectively. Finally, the brightness profiles suggest that the Panthia unit is about 7% brighter than the surrounding areas, whereas the Meroe unit is 8% fainter.

6. Meteorite analogue

Whereas the density of Psyche derived in the present work (3.99 ± 0.26 g cm−3) is incompatible at the 3-sigma level with the one of iron meteorites (∼7.8 g cm−3), it appears fully consistent with that of stony-iron meteorites such as mesosiderites (∼4.25 g cm−3; Britt & Consolmagno 2003) or the Steinbach meteorite (∼4.1 g cm−3; Britt & Consolmagno 2003).

Both mesosiderites and the unique Steinbach meteorite consist of a mixture of metal and pyroxene (orthopyroxene in the case of mesosiderites), a similar composition to that inferred for the surface of Psyche. Indeed, Vernazza et al. (2009) originally did not exclude a possible link between Psyche and mesosiderites on the basis of similar visible and near-infrared spectral properties, and recent laboratory measurements and observations (Cloutis et al. 2017; Sanchez et al. 2017) reinforce this finding. Hence, there is merit to the initial hypothesis by Davis et al. (1999), based on numerical simulations, that Psyche could be a plausible candidate parent body for mesosiderites.

Acknowledgments

Some of the work presented here is based on observations collected 1) at the European Organization for Astronomical Research in the Southern Hemisphere under ESO programs 086.C-0785 (PI Carry) and 199.C-0074 (PI Vernazza); 2) by SuperWASP (DR1) (Butters et al. 2010) as provided by the WASP consortium, and the computing and storage facilities at the CERIT Scientific Cloud, reg. no. CZ.1.05/3.2.00/08.0144 which is operated by Masaryk University, Czech Republic; and 3) the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. This research has made use of the Keck Observatory Archive (KOA), which is operated by the W. M. Keck Observatory and the NASA Exoplanet Science Institute (NExScI), under contract with the National Aeronautics and Space Administration. M. Viikinkoski and M. Kaasalainen were supported by the Academy of Finland Centre of Excellence in Inverse Problems. B. Carry, A. Drouard, J. Grice and P. Vernazza were supported by CNRS/INSU/PNP. J. Hanus and J. Durech were supported by the grant 18-09470S of the Czech Science Foundation. This work has been supported by Charles University Research program No. UNCE/SCI/023. F. Marchis was supported by the National Science Foundation under Grant No 1743015. The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no 687378. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. Thanks to all the amateurs worldwide who regularly observe asteroid lightcurves and stellar occultations. Co-authors of this study are amateurs who observed Psyche, and provided crucial data. The authors acknowledge the use of the Virtual Observatory tools Miriade: http://vo.imcce.fr/webservices/miriade/ (Berthier et al. 2008), TOPCAT: http://www.star.bris.ac.uk/~mbt/topcat/, and STILTS: http://www.star.bris.ac.uk/~mbt/stilts/ (Taylor 2005). This research used the SSOIS: http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/ssois facility of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency (Gwyn et al. 2012).

References

  1. Avdellidou, C., Delbo’, M., & Fienga, A. 2018, MNRAS, 475, 3419 [NASA ADS] [CrossRef] [Google Scholar]
  2. Baer, J., & Chesley, S. R. 2017, AJ, 154, 76 [NASA ADS] [CrossRef] [Google Scholar]
  3. Baer, J., Milani, A., Chesley, S. R., & Matson, R. D. 2008, BAAS, 40, 493 [NASA ADS] [Google Scholar]
  4. Baer, J., Chesley, S. R., & Matson, R. D. 2011, AJ, 141, 143 [NASA ADS] [CrossRef] [Google Scholar]
  5. Berthier, J., Hestroffer, D., Carry, B., et al. 2008, LPI Contrib., 1405, 8374 [NASA ADS] [Google Scholar]
  6. Beuzit, J.-L., Feldt, M., Dohlen, K., et al. 2008, Proc. SPIE, 7014, 701418 [CrossRef] [Google Scholar]
  7. Britt, D. T., & Consolmagno, G. J. 2003, Meteorit. Planet. Sci., 38, 1161 [NASA ADS] [CrossRef] [Google Scholar]
  8. Butters, O. W., West, R. G., Anderson, D. R., et al. 2010, A&A, 520, L10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Carry, B. 2012, Planet. Space Sci., 73, 98 [NASA ADS] [CrossRef] [Google Scholar]
  10. Carry, B., Kaasalainen, M., Merline, W. J., et al. 2012, Planet. Space Sci., 66, 200 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Carry, B., Vachier, F., Berthier, J., & Team L. 2018, A&A, submitted [Google Scholar]
  12. Chang, Y. C., & Chang, C. S. 1963, Acta Astron. Sin., 11, 139 [Google Scholar]
  13. Cloutis, E. A., Applin, D. M., Kiddell, C., et al. 2017, Lunar Planet. Sci. Conf., 48, 1228 [NASA ADS] [Google Scholar]
  14. Davis, D. R., Farinella, P., & Marzari, F. 1999, Icarus, 137, 140 [NASA ADS] [CrossRef] [Google Scholar]
  15. Dollfus, A., & Geake, J. A. 1977, Eng. Sci., 285, 397 [Google Scholar]
  16. Dollfus, A., Auriere, M., & Santer, R. 1979, AJ, 84, 1419 [NASA ADS] [CrossRef] [Google Scholar]
  17. Dotto, E., Barucci, M. A., Fulchignoni, M., et al. 1992, A&AS, 95, 195 [NASA ADS] [Google Scholar]
  18. Drummond, J., & Christou, J. 2008, Icarus, 197, 480 [NASA ADS] [CrossRef] [Google Scholar]
  19. Drummond, J. D., Merline, W. J., Carry, B., et al. 2018, Icarus, 305, 174 [NASA ADS] [CrossRef] [Google Scholar]
  20. Dunham, D. W., Herald, D., Frappa, E., et al. 2017, Asteroid Occultations, NASA Planetary Data System, EAR-A-3-RDR-OCCULTATIONS-V15.0 [Google Scholar]
  21. Ďurech, J., Kaasalainen, M., Herald, D., et al. 2011, Icarus, 214, 652 [NASA ADS] [CrossRef] [Google Scholar]
  22. Elkins-Tanton, L. T., Asphaug, E., Bell, J. F., et al. 2017, Lunar Planet. Sci. Conf., 48, 1718 [NASA ADS] [Google Scholar]
  23. Fienga, A., Laskar, J., Morley, T., et al. 2009, A&A, 507, 1675 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Fienga, A., Laskar, J., Kuchynka, P., et al. 2011, Celest. Mech. Dyn. Astron., 111, 363 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  25. Fienga, A., Manche, H., Laskar, J., Gastineau, M., & Verma, A. 2013, ArXiv e-prints [arXiv:1301.1510] [Google Scholar]
  26. Fienga, A., Manche, H., Laskar, J., Gastineau, M., & Verma, A. 2014, Sci. Notes [Google Scholar]
  27. Folkner, W. M., Williams, J. G., & Boggs, D. H. 2009, IPN Prog. Rep., 42, 1 [Google Scholar]
  28. Fusco, T., Mugnier, L. M., Conan, J. M., et al. 2003, in Adaptive Optical System Technologies II, eds. P. L. Wizinowich, & D. Bonaccini, Proc. SPIE, 4839, 1065 [NASA ADS] [CrossRef] [Google Scholar]
  29. Goffin, E. 2014, A&A, 565, A56 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Grice, J., Snodgrass, C., Green, S. F., Parley, N. R., & Carry, B. 2017, Asteroids, Comets, and Meteors: ACM 2017 [Google Scholar]
  31. Gwyn, S. D. J., Hill, N., & Kavelaars, J. J. 2012, Publ. Astron. Soc. Pac., 124, 579 [NASA ADS] [CrossRef] [Google Scholar]
  32. Hanuš, J., Marchis, F., & Ďurech, J. 2013, Icarus, 226, 1045 [NASA ADS] [CrossRef] [Google Scholar]
  33. Hanuš, J., Viikinkoski, M., Marchis, F., et al. 2017, A&A, 601, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Hapke, B. 1984, Icarus, 59, 41 [NASA ADS] [CrossRef] [Google Scholar]
  35. Hardersen, P. S., Gaffey, M. J., & Abell, P. A. 2005, Icarus, 175, 141 [NASA ADS] [CrossRef] [Google Scholar]
  36. Harris, A. W., Young, J. W., Bowell, E., & Tholen, D. J. 1999, Icarus, 142, 173 [NASA ADS] [CrossRef] [Google Scholar]
  37. Ivantsov, A. 2008, Planet. Space Sci., 56, 1857 [NASA ADS] [CrossRef] [Google Scholar]
  38. Kaasalainen, M., Torppa, J., & Muinonen, K. 2001, Icarus, 153, 37 [NASA ADS] [CrossRef] [Google Scholar]
  39. Kaasalainen, M., Torppa, J., & Piironen, J. 2002, Icarus, 159, 369 [NASA ADS] [CrossRef] [Google Scholar]
  40. Kochetova, O. M. 2004, Sol. Sys. Res., 38, 66 [NASA ADS] [CrossRef] [Google Scholar]
  41. Kochetova, O. M., & Chernetenko, Y. A. 2014, Sol. Sys. Res., 48, 295 [NASA ADS] [CrossRef] [Google Scholar]
  42. Konopliv, A. S., Asmar, S. W., Folkner, W. M., et al. 2011, Icarus, 211, 401 [NASA ADS] [CrossRef] [Google Scholar]
  43. Krasinsky, G. A., Pitjeva, E. V., Vasiliev, M. V., & Yagudina, E. I. 2001, Communications of IAA of RAS [Google Scholar]
  44. Kuchynka, P., & Folkner, W. M. 2013, Icarus, 222, 243 [NASA ADS] [CrossRef] [Google Scholar]
  45. Kuzmanoski, M., & Kovačević, A. 2002, A&A, 395, L17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Landsman, Z. A., Emery, J. P., Campins, H., et al. 2018, Icarus, 304, 58 [NASA ADS] [CrossRef] [Google Scholar]
  47. Lupishko, D. F., Kiselev, N. N., Chernova, G. P., & Belskaya, I. N. 1980, Pisma v Astron. Zh., 6, 184 [NASA ADS] [Google Scholar]
  48. Lupishko, D. F., Belskaia, I. N., Tupieva, F. A., & Chernova, G. P. 1982, Astron. Vestnik, 16, 101 [NASA ADS] [Google Scholar]
  49. Margot, J. L., Pravec, P., Taylor, P., Carry, B., & Jacobson, S. 2015, Asteroid Systems: Binaries, Triples, and Pairs (Univ. Arizona Press), 355 [Google Scholar]
  50. Masiero, J. R., Mainzer, A. K., Grav, T., et al. 2012, ApJ, 759, L8 [NASA ADS] [CrossRef] [Google Scholar]
  51. Matter, A., Delbo, M., Carry, B., & Ligori, S. 2013, Icarus, 226, 419 [NASA ADS] [CrossRef] [Google Scholar]
  52. Moffat, A. 1969, A&A, 3, 455 [NASA ADS] [Google Scholar]
  53. Morrison, D., & Zellner, B. 2007, NASA Planet. Data Sys. [Google Scholar]
  54. Mugnier, L. M., Fusco, T., & Conan, J.-M. 2004, JOSA A, 21, 1841 [NASA ADS] [CrossRef] [Google Scholar]
  55. Neely, A. W. 1992, Minor Planet Bull., 19, 28 [NASA ADS] [Google Scholar]
  56. Ockert-Bell, M. E., Clark, B. E., Shepard, M. K., et al. 2008, Icarus, 195, 206 [NASA ADS] [CrossRef] [Google Scholar]
  57. Pfleiderer, J., Pfleiderer, M., & Hanslmeier, A. 1987, A&AS, 69, 117 [NASA ADS] [Google Scholar]
  58. Pitjeva, E. V. 2013, Sol. Sys. Res., 47, 386 [NASA ADS] [CrossRef] [Google Scholar]
  59. Pitjeva, E. V. 2010, in IAU Symp., eds. S. A. Klioner, P. K. Seidelmann, & M. H. Soffel, 261, 170 [NASA ADS] [Google Scholar]
  60. Reddy, V., Le Corre, L., O’Brien, D. P., et al. 2012, Icarus, 221, 544 [NASA ADS] [CrossRef] [Google Scholar]
  61. Ryan, E. L., & Woodward, C. E. 2010, AJ, 140, 933 [NASA ADS] [CrossRef] [Google Scholar]
  62. Sanchez, J. A., Reddy, V., Shepard, M. K., et al. 2017, AJ, 153, 29 [NASA ADS] [CrossRef] [Google Scholar]
  63. Scheeres, D. J., Britt, D., Carry, B., & Holsapple, K. A. 2015, Asteroid Inter. Morphol., 745 [Google Scholar]
  64. Shepard, M. K., Clark, B. E., Ockert-Bell, M., et al. 2010, Icarus, 208, 221 [NASA ADS] [CrossRef] [Google Scholar]
  65. Shepard, M. K., Taylor, P. A., Nolan, M. C., et al. 2015, Icarus, 245, 38 [NASA ADS] [CrossRef] [Google Scholar]
  66. Shepard, M. K., Richardson, J., Taylor, P. A., et al. 2017, Icarus, 281, 388 [NASA ADS] [CrossRef] [Google Scholar]
  67. Somenzi, L., Fienga, A., Laskar, J., & Kuchynka, P. 2010, Planet. Space Sci., 58, 858 [NASA ADS] [CrossRef] [Google Scholar]
  68. Sykes, M., Brown, T., & Raugh, A. 1998, NASA Planet. Data Sys. [Google Scholar]
  69. Takir, D., Reddy, V., Sanchez, J. A., Shepard, M. K., & Emery, J. P. 2017, AJ, 153, 31 [NASA ADS] [CrossRef] [Google Scholar]
  70. Taylor, R. C. 1977, AJ, 82, 441 [NASA ADS] [CrossRef] [Google Scholar]
  71. Taylor, M. B. 2005, in Astronomical Data Analysis Software and Systems XIV, eds. P. Shopbell, M. Britton, & R. Ebert, ASP Conf. Ser., 347, 29 [NASA ADS] [Google Scholar]
  72. Tedesco, E. F., & Taylor, R. C. 1985, Icarus, 61, 241 [NASA ADS] [CrossRef] [Google Scholar]
  73. Tedesco, E. F., Taylor, R. C., Drummond, J., et al. 1983, Icarus, 54, 30 [NASA ADS] [CrossRef] [Google Scholar]
  74. Thalmann, C., Schmid, H. M., Boccaletti, A., et al. 2008, Proc. SPIE, 7014, 70143F [CrossRef] [Google Scholar]
  75. Usui, F., Kuroda, D., Müller, T. G., et al. 2011, PASJ, 63, 1117 [NASA ADS] [CrossRef] [Google Scholar]
  76. van Houten-Groeneveld, I., & van Houten, C. J. 1958, ApJ, 127, 253 [NASA ADS] [CrossRef] [Google Scholar]
  77. Vasiliev, M. V., & Yagudina, E. I. 1999, Communications of IAA of RAS [Google Scholar]
  78. Vernazza, P., Brunetto, R., Binzel, R. P., et al. 2009, Icarus, 202, 477 [NASA ADS] [CrossRef] [Google Scholar]
  79. Vernazza, P., Castillo-Rogez, J., Beck, P., et al. 2017, AJ, 153, 72 [NASA ADS] [CrossRef] [Google Scholar]
  80. Vernazza, P., Jorda, L., Hanuš, J., et al. 2018, A&A, in press [Google Scholar]
  81. Viateau, B. 2000, A&A, 354, 725 [NASA ADS] [Google Scholar]
  82. Viikinkoski, M. 2016, PhD Thesis, Tampere University of Technology [Google Scholar]
  83. Viikinkoski, M., & Kaasalainen, M. 2014, Inverse Prob. Imaging, 8, 885 [CrossRef] [Google Scholar]
  84. Viikinkoski, M., Kaasalainen, M., & Ďurech, J. 2015, A&A, 576, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Viswanathan, V., Fienga, A., Gastineau, M., & Laskar, J. 2017, Notes Scientifiques et Techniques de l’Institut de Mécanique Céleste, 39, 108 [Google Scholar]
  86. Warner, B. D. 2016, Minor Planet Bull., 43, 137 [NASA ADS] [Google Scholar]
  87. Weidenschilling, S. J., Chapman, C. R., Davis, D. R., et al. 1987, Icarus, 70, 191 [NASA ADS] [CrossRef] [Google Scholar]
  88. Weidenschilling, S. J., Chapman, C. R., Davis, D. R., Greenberg, R., & Levy, D. H. 1990, Icarus, 86, 402 [NASA ADS] [CrossRef] [Google Scholar]
  89. Yang, B., Wahhaj, Z., Beauvalet, L., et al. 2016, ApJ, 820, L35 [NASA ADS] [CrossRef] [Google Scholar]
  90. Zhou, X.-H., & Yang, X.-Y. 1981, Acta Astron. Sin., 22, 378 [NASA ADS] [Google Scholar]
  91. Zhou, X. H., Yang, X. Y., & Wu, Z. X. 1982, Acta Astron. Sin., 23, 349 [NASA ADS] [Google Scholar]
  92. Zielenbach, W. 2011, AJ, 142, 120 [NASA ADS] [CrossRef] [Google Scholar]

Appendix A: Additional tables and figures

thumbnail Fig. A.1.

All 25 VLT/SPHERE/ZIMPOL images of Psyche obtained at five different epochs and deconvolved with the Mistral algorithm and a parametric PSF with a Moffat shape.
Open with DEXTER

thumbnail Fig. A.2.

Observed occultation chords and the model silhouettes.
Open with DEXTER

thumbnail Fig. A.3.

Shape model of Psyche. Viewing directions are from positive x, y, and z axes, respectively.
Open with DEXTER

thumbnail Fig. A.4.

Albedo map distribution derived from the model showing the presence of a dark region near the equator (Meroe) and a bright feature near the poles at a longitude centered to 270 deg.
Open with DEXTER

thumbnail Fig. A.5.

Available mass estimates for Psyche compiled from the literature. See Table A.1 for more details.
Open with DEXTER

thumbnail Fig. A.6.

Available diameter estimates for Psyche compiled from the literature. See Table A.2 for more details.
Open with DEXTER

Table A.1.

Mass estimates (ℳ) of (16) Psyche collected in the literature.

Table A.2.

Diameter estimates (𝒟) of (16) Psyche collected in the literature.

Table A.3.

Stellar occultations by Psyche.

All Tables

Table 1.

Physical parameters of Psyche derived in this study compared with previous works.

In the text
Table A.1.

Mass estimates (ℳ) of (16) Psyche collected in the literature.

In the text
Table A.2.

Diameter estimates (𝒟) of (16) Psyche collected in the literature.

In the text
Table A.3.

Stellar occultations by Psyche.

In the text

All Figures

thumbnail Fig. 1.

First row: VLT/SPHERE/ZIMPOL images of Psyche obtained at five different epochs (ordered by rotation phase) and deconvolved with the Mistral algorithm and a parametric PSF with a Moffat shape (Sect. 2). Second row: corresponding shape model projections. Third row: corresponding projections using the shape model of Shepard et al. (2017). The arrow shows the direction of the rotation axis. Last row: images with contrast and size enhanced to highlight the albedo variegation. The two topographic regions that we consider as real features are also indicated.
Open with DEXTER
In the text
thumbnail Fig. A.1.

All 25 VLT/SPHERE/ZIMPOL images of Psyche obtained at five different epochs and deconvolved with the Mistral algorithm and a parametric PSF with a Moffat shape.
Open with DEXTER
In the text
thumbnail Fig. A.2.

Observed occultation chords and the model silhouettes.
Open with DEXTER
In the text
thumbnail Fig. A.3.

Shape model of Psyche. Viewing directions are from positive x, y, and z axes, respectively.
Open with DEXTER
In the text
thumbnail Fig. A.4.

Albedo map distribution derived from the model showing the presence of a dark region near the equator (Meroe) and a bright feature near the poles at a longitude centered to 270 deg.
Open with DEXTER
In the text
thumbnail Fig. A.5.

Available mass estimates for Psyche compiled from the literature. See Table A.1 for more details.
Open with DEXTER
In the text
thumbnail Fig. A.6.

Available diameter estimates for Psyche compiled from the literature. See Table A.2 for more details.
Open with DEXTER
In the text

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