Poster presentations and abstracts

At present, the main database of asteroid absolute magnitudes is the Minor Planet Center (MPC). The MPC receives asteroid magnitudes from many observatories in different spectral bands of different photometric systems, but calculates absolute magnitudes ​​for V band of the Johnson photometric system using the HG-function [1]. A comparison of the MPC absolute magnitudes (H_MPC) with the dataset of high-quality absolute magnitudes obtained by Pravec et al. [2] revealed systematical differences between these datasets. The new HG₁G₂ function was recommended by Muinonen et al. [3] to be used for more precise calculating the asteroid absolute magnitudes. This function was calibrated according to good measured magnitude-phase dependences, and the average parameters ​​for major taxonomic classes of asteroids were obtained [4, 5]. Using these average values, it is possible to calculate the absolute magnitudes of asteroids from individual observations at different phase angles. For sparse photometric data, it was suggested to use the two-parameter HG₁₂ system [3, 4, 6].

Recently, large-scale survey programs such as Pan-STARRS and Palomar Transient Factory (PTF) [7, 8] performed new observations, within which absolute magnitudes of asteroids have also been derived. Both datasets are homogeneous, but they were obtained mostly in the Sloan system and then transformed to the Johnson system. Comparison of these datasets of absolute magnitudes ​​with the MPC data showed systematical deviations [7, 8]. It should be noted that the absolute magnitudes of asteroids in the Pan-STARRS and PTF surveys [7, 8] were obtained in the new HG₁₂ system. In addition, the work is continued to obtain the asteroid absolute magnitudes in the new system for PTF and Pan-STARRS datasets [9]. To check the reliability of these datasets and identify systematic deviations, an independent set of high-quality data on absolute magnitudes is required. Thus, we initiated an observational program to determine the absolute magnitudes of several hundred asteroids. Here we present preliminary results of our program.

For our dataset, we used first the data of the asteroid magnitude-phase dependences collected at the Institute of Astronomy of V. N. Karazin Kharkiv National University [5, 10–12]. We also used observational data of asteroids from our other programs and performed new observations of some asteroids [13–14]. We calculated our absolute magnitude dataset in the Johnson V band with a correction for lightcurve variations using the HG₁G₂ system according to its extension to low-accuracy data [4]. In such manner, we obtained a homogenous dataset of absolute magnitudes of about 300 asteroids in the range from 6.5 to 16 mag. Fig. 1 shows the correlations of the absolute magnitudes from the MPC, Pan-STARRS (H_PS), and PTF (H_PTF) datasets with those of the Kharkiv dataset (H_KH).

Figure 1: Absolute magnitudes of MPC (H_MPC), Pan-STARRS (H_PS), and PTF (H_PTF) vs. those of the Kharkiv dataset (H_KH).

We observe a high correlation between our dataset and the other three datasets. There are small differences in the constant terms and slopes that point out some systematical deviations especially for the MPC and the PTF datasets.

Figure 2: Histogram of the absolute magnitude deviations between the Kharkiv and Pan-STARRS datasets.

Figure 3: Histogram of the absolute magnitude deviations between the Kharkiv and MPC datasets.

For the MPC dataset, we found a systematical deviation of about 0.1 mag (Fig. 3). It is less than that obtained in [2] and close to that obtained in [7]. The smallest deviations were found for the Pan-STARRS dataset. This is confirmed by the histogram of absolute magnitude deviations between the Kharkiv and Pan-STARRS datasets presented in Fig. 2. The solid line is a fit to the data using the Gaussian function.

We prepared a preliminary comparative analysis of the asteroid absolute magnitudes between the Kharkiv dataset and the MPC, Pan-STARRS, and PTF datasets. The analysis has shown that the absolute magnitude dataset obtained from the Pan-STARRS survey project is closest to our dataset and can be the most suitable for the determination of diameters or albedos of asteroids.

Acknowledgments

This research has made use of data and/or services provided by the International Astronomical Union's Minor Planet Center.

References

[1] Bowell, E., Hapke, B., Domingue, D., et al. In Asteroids II. Tucson. Univ. Arizona Press. P. 524−556, 1989.

[2] Pravec, P., Harris, A.W., Kušnirák, P., et al. Icarus, 221, 365-387, 2012.

[3] Muinonen, K., Belskaya, I.N., Cellino, A., et al. Icarus, 209, 542–555, 2010.

[4] Penttilä, A. Shevchenko, V.G., Wilkman, O., Muinonen, K. PSS, 123, 117–125, 2016.

[5] Shevchenko, V.G., Belskaya, I.N., Muinonen, K., et al. PSS, 123, 101−116, 2016.

[6] Oszkiewicz, D.A., Muinonen, K., Bowell, E., et al. JQSRT, 112, 1919-1929, 2011.

[7] Veres, P., Jedicke, R., Firzsimmons, A., et al. Icarus, 261, 34-47, 2015.

[8] Waszczak, A., Chang, C.-K., Ofeck, E.O., et al. Astron. J., 150, A75, 2015.

[9] Penttilä, A., Cellino A., Lu X., et al. DPS, 48, No 7, 228, 2016.

[10] Shevchenko, V.G., Belskaya I.N., Lupishko D.F., et al. EAR-A-COMPIL-3-MAGPHASE-V1.0. NASA PDS, 2010.

[11] Shevchenko, V.G., Belskaya, I.N., Slyusarev, I.G., et al. Icarus, 217, 202–208, 2012.

[12] Slyusarev, I.G., Shevchenko, V.G., Belskaya, I.N., et al. LPSC 43, 1885, 2012.

[13] Chiorny, V.G., Shevchenko V.G., Krugly Yu.N., et al. PSS, 55, 986−997, 2007.  

[14] Shevchenko, V.G., Mikhalchenko O.I., Belskaya I.N., et al. LPSC 50, Abstract #1771, 2019.

How to cite: Shevchenko, V. G., Mikhalchenko, O. I., Belskaya, I., Chiorny, V., Krugly, Y., Slyusarev, I., Hromakina, T., Dovgopol, A., Gritsevich, M., Muinonen, K., and Penttilä, A.: Comparative analysis of the absolute magnitudes of asteroids with MPC, Pan-STARRS, and PTF datasets, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-565, https://doi.org/10.5194/epsc2020-565, 2020.

• Abstract

From July 2011 to September 2012, the Dawn spacecraft orbited the large asteroid (4) Vesta. A nearly global coverage of the surface was achieved by the Visible and InfraRed mapping spectrometer (VIR) [1]. The change of CCD temperature during the data acquisition affects the instrument spectral response. As a consequence, the acquired spectra experienced a change in spectral slope. Here we present a method to correct this issue; this method is similar to the one we applied for the VIR VIS data of Ceres [2].

• CCD detector temperature dependencies

The acquisition of VIR data are organized in sequences during which several hyperspectral cubes are acquired. Within a given sequence, which may last several hours, the CCD temperature increases over time, while it goes back down in the time period between two sequences. The increase of CCD temperature induces a reddening of the spectra at visible wavelengths, as displayed in Fig. 1. To avoid misinterpretation and to retrieve a coherent shape of the Vesta spectra, a correction is therefore required.

Figure 1 – Median spectral variations observed for each CCD temperature recorded during the VH2 mission phase (normalized at 550nm).

• Spectral correction

As in [2], we defined a correction factor for the different mission phases at Vesta, considering their particularities if necessary. This correction factor requires the definition of a reference temperature, at which the spectral response is considered reliable, and a corresponding reference spectrum. A previous analysis of the same effect on Ceres observations [2], revealed a reference CCD temperature of 177K. The correction has been applied on the mission phases during which VIR acquired VIS data. It allows to obtain a globally coherent dataset. Mapping and reliable spectral studies are then possible (see Fig. 2).

Figure 2 - Map of the spectral slope for the VH2 mission phase before (top) and after (bottom) the correction. The gradient observed, in each sequences, in the top panel vanishes in the bottom panel, after the application of the correction.

• Conclusion

The empirical method developed in [2] has now been applied to the Vesta data acquired by the VIS channel of the VIR spectrometer. This correction is mandatory to carry out reliable analysis of the Vesta surface at global and local scale.

• References

[1] De Sanctis, M. C., Coradini, A., et al. 2011, Space Science Reviews, 163, 329,

[2] Rousseau, B., Raponi, A., Ciarniello, M., et al. 2019, Review of Scientific Instruments, 90,

How to cite: Rousseau, B., De Sanctis, M. C., Ciarniello, M., Raponi, A., Scarica, P., Ammannito, E., Frigeri, A., Carrozzo, F. G., Tosi, F., and Fonte, S.: Dawn/VIR at Vesta: correction of the spectral variations induced by CDD temperature change on the visible channel, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-116, https://doi.org/10.5194/epsc2020-116, 2020.

Abstract

We aim at retrieving physical and compositional surface properties of the nucleus of comet 67P/Churyumov-Gerasimenko (hereafter 67P) from VIS-IR hyperspectral images (‘cubes’). Here we report on our progress in the geometric modeling and spectral fitting.

Introduction

The measured cubes have been acquired with VIRTIS-M instrument [1] aboard Rosetta. Starting from a digital shape model of 67P [2], the radiance measured by a pixel results from sub-pixel radiance contributions of several shape model facets weighted by the wavelength-dependent spatial point spread function (PSF) of the instrument. Based on a corresponding sub-pixel geometric modeling, we have computed the weighting coefficients and quantified the PSF [3]. The radiance contribution from a single facet can be simulated from physical and compositional parameters defined on this facet, using a photometric model (Hapke or Shkuratov [4,5]). Besides facilitating the consideration of PSF effects, this approach allows us to better approximate the rugged fine-scale topography of 67P that leads to varying observation/illumination/shadowing conditions on sub-pixel scales. Also, the retrieval of parameters common to multiple acquisitions requires a definition of surface parameters bound to the shape model instead of the respective pixel footprints. In this work, we focus on the spectral fitting of entire sets of cubes.

Retrieval algorithm

The Bayesian Multi-Spectrum Retrieval algorithm MSR [6] fits the synthetic spectra to the measured ones by iteratively varying the facet properties. MSR takes into account constraints and Bayesian a priori information on the facet properties (mean values, standard deviations, correlation lengths/times/wavelengths) as well as measurement error information. Moreover, consistency requirements are respected. For instance, for measurements acquired at similar times, there are facet properties (surface roughness, particle size, composition, etc.) that do not change between repeated observations and can be treated and retrieved as common to those observations. To achieve this, MSR regards many measurements of a selected surface region under different illumination and observation conditions, shadowing state, spatial resolution, and at different wavelengths as a single meta-measurement. This is analogous to the meta-measurement formed by measurements at different single wavelengths, called a spectrum.

Preliminary results

At the present stage, MSR is tested to retrieve maps of shape model facet properties from a meta-measurement encompassing tens of VIRTIS-M-cubes, corresponding to the order of a million measured spectra, see Fig. 1.

Panel (a) shows a calibrated and pre-processed measured VIRTIS-M cube, represented at 1 µm, mainly showing 67P’s northern hemisphere.

Panel (b) illustrates residual variations when the first-order effect of the complex topography of 67P is removed using the Akimov disk function [5], which describes the photometry of utterly rough surfaces. These residual variations can be due to limits in the applicability of the Akimov disk function or real physical variations in texture or composition. The phase angle (~40°) is too large for substantial opposition effects to show up.

In the case where we fit the entire cube using the Hapke model, panel (c) shows very little residual variations that are mainly associated with local terminators and slight PSF model imprecisions. This image illustrates that our present setup allows us to fit the measurements very accurately when the facet properties can vary freely within the frame of our Bayesian regularization for one cube. For this comparison, we selected as free parameters the single-scattering albedo and phase function asymmetry parameter spectra for two intimately mixed endmembers as properties that are constant over all facets, and the relative abundance of the two endmembers along with the roughness angle and filling factor as properties that can vary between facets.

Finally, panel (d) exhibits moderate residual variations. Here, we fitted a meta-measurement of 25 cubes at various illumination and observation conditions and displayed the one also represented in the other panels. The fitting took about one week on a desktop computer. Now the facet properties cannot vary as freely as for case (c), because simultaneously they also have to be compatible with all other considered cubes. The retrieved facet properties are therefore more well-grounded candidates for the actual surface properties. This panel demonstrates that using information from many hyperspectral images helps to reduce overfitting. We also note that the here utilized set of free parameters is not able to fully capture the spectral variability of the measurements within the frame of this model, pointing to the necessity of additional free parameters, or difficulties of the Hapke model to simultaneously parameterize the different cubes in a consistent way.

At this stage, it is not clear yet, how the retrieved facet properties are related to actual surface properties of 67P. At the meeting we will present different parameter sets that lead to equally well fits and discuss their plausibility. A similar investigation based on the Shkuratov model as well as a detailed error analysis, based on synthetic VIRTIS-M cubes, to investigate interferences between the retrieved and other parameters, are the next steps in this ongoing work. Finally, we expect our approach to be capable of identifying local surface property variations in a physically and mathematically well-grounded way, and we will investigate their correlations with morphologic regions on 67P.

Acknowledgements

We thank the following institutions and agencies for support of this work: Italian Space Agency (ASI, Italy). Centre National d'Etudes Spatiales (CNES, France), DLR (Germany). D.K. acknowledges DFG-grant KA 3757/2-1.

References

[1] Coradini et al. (2007) Space Sci. Rev. 128, 529. [2] Preusker et al. (2017) A&A 607, L1. [3] Kappel et al. (2019) EPSC-DPS 2019, EPSC-DPS2019-456. [4] Hapke (2012) Theory of Reflectance and Emittance Spectroscopy, 2nd edn. (Cambridge University Press). [5] Shkuratov et al. (2011) Planet. Space Sci. 59, 1326. [6] Kappel (2014) J. Quant. Spectrosc. Rad. 133, 153.

How to cite: Kappel, D., Arnold, G., Moroz, L., Raponi, A., Ciarniello, M., Tosi, F., Erard, S., Rousseau, B., Leyrat, C., Filacchione, G., and Capaccioni, F.: Sub-pixel geometric modeling and spectral fitting for Rosetta/VIRTIS-M measurements of the nucleus of comet 67P, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-226, https://doi.org/10.5194/epsc2020-226, 2020.

We developed a fast algorithm to model a rough planetary surface by means of a statistical multi-facets approach. Consistently with Hapke theory (2012) we model the surface with a distribution of non-spatially resolved facets, being the distribution of their slopes completely described by roughness parameter 𝜃̅.
The parameters in input to model the effect of the surface roughness are the average incidence <i>, emission <e>, and phase <g> angles as inferred from shape model, illumination direction and spacecraft attitude.
Differently from the Hapke model, which is based on analytic relations, here we simulate N facets each with its own viewing geometry (i, e, g), derived from the distribution of slopes (given by 𝜃̅), and from a uniform distribution of rotations of the facets around the axis normal to the average surface. The numerical approach allows us to explore the cases of surfaces with large roughness (𝜃̅>20°) which cannot be described accurately in Hapke model. Once the distributions of i, and e are obtained, significant quantities (e.g. weighted average cosines of illumination and viewing angles), relevant for reflectance modeling, can be calculated as a function of 𝜃̅. Furthermore, the reduction of the scattered power with respect to a smooth surface, produced by the presence of tilted and projected shadows, is computed, similarly to the shadowing function used by the Hapke model.
Our algorithm is capable to model many facets (several thousands) in a short processing time (fractions of a second with a common laptop). The large number of modeled facets ensures a statistically robust result.
This model has two major applications:
- Characterization of the surface roughness by means of best fitting procedures. The correct retrieval of the roughness parameter 𝜃̅ in turn would allow a better retrieval of other quantities relevant for the estimate of additional surface properties (e.g. single scattering albedo, single particle phase function).
- The calculation of the viewing geometry for each facet allows to obtain histograms of relevant parameters for remote sensing applications. As an example, an accurate description of the distribution of the incidence angles on a rough surface is important for thermal modeling applications, since the quantity ⁴√𝑐𝑜𝑠(ⅈ) in first approximation is proportional to the temperature. This would allow a synergy between thermal emission and reflectance modeling to obtain an estimate of the roughness parameter. Moreover, it would allow the retrieval of a distribution of temperatures along the non-resolved facets. The latter would be important in determining the possible presence and extension of cold trap prone to host ices with direct application to unresolved Permanent Shadowed Regions (PSRs) present on polar regions of Mercury and Moon (Lawrence, 2016).
Here we show a comparison of the output of our computation with the outcomes of the Hapke model, and an application of the present method to characterize the temperature distribution in a rough surface.

Acknowledgments. We thank IAPS (Institute for Space Astrophysics and Planetology) for support of this work.

References:
Hapke B., 2012, Theory of Reflectance and Emittance Spectroscopy, Cambridge Univ. Press
Lawrence D. J., 2016, Journal of Geophysical Research: Planets, 122, pp. 21-52

How to cite: Raponi, A., Ciarniello, M., Filacchione, G., and Capaccioni, F.: Modeling non-resolved rough planetary surfaces by means of a statistical multi-facets approach, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-761, https://doi.org/10.5194/epsc2020-761, 2020.

The spherical wavelet based on the lifting scheme is introduced for adaptive discrete-ordinate sampling of the radiation fields, particularly,  in the radiative transfer computation using iterative schemes. The lifting scheme for wavelet transform is described from an implementation point of view, including the construction of hierarchical geodesic grids on the sphere and wavelet constructions. In addition, we compare the method with the conventional spherical harmonics, numerically investigating the transformation error and efficiency. The transformation matrices are built in the least-squares sense. The results demonstrate the feasibility of using spherical wavelets as an adaptive discrete-ordinate sampling method at the cost of O(N), where N is the number of significant coefficients. 

How to cite: Xu, G. and Muinonen, K.: The spherical-wavelet techniques for RT simulation and analysis, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-981, https://doi.org/10.5194/epsc2020-981, 2020.

We are compiling a service, which will produce almost real-time estimates of the Earth Bond albedo in visual and near-infrared wavelengths using the multi-wavelength images from the NOAA Deep Space Climate Observatory’s (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The service will be based on the known land and sea surface types of the Earth and the angular distribution models (ADMs) from the Clouds and the Earth’s Radiant Energy System (CERES) project (some details in Ihalainen, M.Sc. thesis, 2019, University of Helsinki, Finland). Instead of applying the CERES ADM albedo estimates as such, we will only use the ADM shapes for different land and sea surface types, but scale them using the corresponding observations from the EPIC camera.

As the EPIC camera will produce imaging of the Earth disk from the L1 point, in about every two hours and in 10 narrow band filters between 317–780 nm, we can follow the temporal changes in the Bond albedo both in short times scales and over longer periods. We will employ a custom-made classifier for cloud coverage over the Earth disk using the 10 bands in the EPIC images to improve the Bond albedo estimation with temporally suitable cloud coverage ADMs over the land or sea surface pixels.

The service will be run automatically in a dedicated server, and will produce the Bond albedo estimates via a public web interface.