Europlanet Science Congress 2020
Virtual meeting
21 September – 9 October 2020
Europlanet Science Congress 2020
Virtual meeting
21 September – 9 October 2020

Oral presentations and abstracts


Electromagnetic scattering phenomena play a key role in determining the properties of Solar System surfaces based on observations using different techniques and in a variety of wavelengths ranging from the ultraviolet to the radio. This session will promote a general advancement in the exploitation of observational and experimental techniques to characterize radiative transfer in complex particulate media. Abstracts are solicited on progresses in numerical methods to extract relevant information from imagery, photometry, polarimetry and spectroscopy in solid phase, reference laboratory databases, photometric modeling, interpreting features on planetary surfaces, mixing/unmixing methods… Software and web service applications are welcome.

Conveners: Stéphane Erard, Maria Gritsevich, Karri Muinonen, Antti Penttilä, Frédéric Schmidt

Session assets

Session summary

Spectroscopy and Bennu
Pedro Henrique Hasselmann, Sonia Fornasier, Maria Antonietta Barucci, Alice Praet, Beth Ellen Clark, Jian-Yang Li, Dathon Golish, Daniella N. DellaGiustina, Prasanna Deshapriya, Xiao-Duan Zou, Mike G. Daly, Olivier S. Barnouin, Amy A. Simon, and Dante S. Lauretta

Introduction: The OSIRIS-REx mission has revealed a dark, boulder-rich, apparently dust-poor surface of the B-type asteroid (101955) Bennu [1], therefore a challenge for bi-directional reflectance (rF) modeling. With an estimated geometric albedo of 4.5% [2], Bennu is darker than many comets, and its reflectance distribution is dominated by single-scattering processes. The general approach to model a dark asteroid’s bi-directional reflectance distribution is to apply the standard Hapke IMSA model and its shadowing function [3]. However, this can imprecisely describe the roughness slopes for rocky surfaces [4]. Assuming that surfaces are fully diffuse can negate a specular forward-scattering contribution from crystalline components in the regolith [7]. To achieve a more complete photometric modeling of Bennu’s scattering curve, we rely on the radiative transfer semi-numerical model of Van Ginneken et al. [8,9].

Observations: MapCam is an optical imager [10] on-board the OSIRIS-REx spacecraft, equipped with four broadband color filters (60-90 nm wide) centered at 473 (b'), 550 (v'), 698 (w') and 847 (x') nm. We analyzed images acquired during the Equatorial Station (EQ), a campaign of the Detailed Survey mission phase [11], over a full rotation of (101955) Bennu at a nadir spatial resolution of ~33 cm/px. EQ comprehended seven phase angle configurations α =[7.5° ,30° ,45° ,90° ,130°] .

We analyzed the pixel subtended by the mission’s candidate sample collection sites, for which highly precise, laser altimeter-based digital terrain models (DTMs) were available [12,13]. These four candidate sites were called Sandpiper (latitude=-47°; longitude=322°), Osprey (11°; 88°), Nightingale ( 56°; 43°), and Kingfisher (11°; 56°). The varied latitude and longitudes provided the range of observational conditions required for our analysis.

Methodology: The methodology consists of the following steps:

a) NAIF SPICE kernels [14] and the DTMs are ingested into a ray-tracing code for rendering shadowed images. This allows us to discount for the effects of macroscopic shadows, leaving only the sub-facet texture to be modeled. These ancillary images provide the incidence, emergence, phase & azimuth for every facet.

b) We apply Van Ginneken’s model to every facet. Occlusion and shadowing as well as the retro-reflection among the reliefs are taken into account. The model has two major components: the analytical expression for the specular reflection; and the numerically-integrated diffusive reflection. At total, the model has three free parameters: ρ (single-scattering albedo), σ (RMS roughness slope), and g (specular-to-diffuse ratio), plus the three more related to the scattering phase function (bi-lobe Henyey-Greenstein function: c, b1, and b2)

c) Inverse problem: We run the Monte Carlo Markov Chain to sample the multi-parametric space in order to reconstruct a posteriori probability distribution of solutions for every free parameter, i.e., (ρ, σ, g, b1, b2, c), from which the statistics for every solution are estimated.

Results: The MCMC technique reveals some interesting aspects of Bennu's surface (Fig. 1): while the RMS roughness slope of 27+1-5 is in line with what has been obtained for other asteroids using Hapke shadowing function, we are puzzled by the indication of a non-zero specular reflection ratio from the surface (2.6+1−0.8 %). The specular reflection hints at a possible mono-crystalline component.

As for the diffuse rough component, the analysis of the photometric correction of OCAMS images taken at varied phase angles (α) indicates a more complex scenario. Up to α = 90°, the photometric correction is vastly improved by mixing two different solutions for roughness (one with low RMS σ and another with global RMS σ), a bi-modality already perceived from the MCMC a posteriori distributions. We have shown that most of Bennu's brightness variation can be explained by tuning the roughness slope statistical distribution.

Finally, we report a back-scatter phase function for the phase angle range between 7.5°, and 130°, without any expressive spectral trend in the visible range. The MCMC inversion hints at a possible second forward-scatter lobe of at least ~0.2 width. This leads to two possible solutions for the asymmetric factor (ξ (1) = −0.360 ± 0.030 and ξ(2) = −0.444 ± 0.020). We also report a dark global approximate single-scattering albedo at 550 nm from the collective analysis of all site candidates of 4.64+0.08-0.09 % . The single-scattering MapCam four-band colors show a similar spectral trend to the global average OVIRS EQ3 spectrum. The four sites together provide a general description of Bennu's colors.

Fig. 1. Parametric solutions after the MCMC technique for all sample sites together, and the scattering phase function (bottom row) for each MapCam filters.


[1] Lauretta, D. et al. Met.. & Plan. Scie., 50, Issue 4, 834-849, 2015.

[2] DellaGiustina, D.N. et al., Nat. Astron. 3, 241-351, 2019.

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[12] Daly, M. G. et al., SSR 212, 899, 2017.

[13] Barnouin, O. S. et al., PLANSS 180, 104764, 2020.

[14] Acton, C. H.. PLANSS 44.1 (1996): 65-70.

[15] Hapke, B. Cambridge Univ. Press, 2ed., 2012.

How to cite: Hasselmann, P. H., Fornasier, S., Barucci, M. A., Praet, A., Clark, B. E., Li, J.-Y., Golish, D., N. DellaGiustina, D., Deshapriya, P., Zou, X.-D., G. Daly, M., S. Barnouin, O., A. Simon, A., and S. Lauretta, D.: Modeling first-order scattering processes from OSIRIS-REx color images of the rough surface of asteroid (101955) Bennu, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-122,, 2020.

Alice Praet, Antonella Barucci, Hannah Kaplan, Frédéric Merlin, Beth Clark, Amy Simon, Victoria Hamilton, Joshua Emery, Ellen Howell, Lucy Lim, Xiao-Duan Zou, Dennis Reuter, Salvatore Ferrone, Prasanna Deshapriya, Sonia Fornasier, Pedro Hasselmann, Giovanni Poggiali, John Brucato, Driss Takir, and Dante Lauretta

OVIRS [1, 2] acquired visible to near-infrared spectra of asteroid Bennu’s surface showing an asymmetric absorption band centered at 2.74 ± 0.01 μm [3], attributed to the presence of hydrated phyllosilicates. This feature is widespread across Bennu’s surface. Such an absorption band has been detected in some carbonaceous chondrite meteorites [4, 5].

In this study, we report the results from two distinct methods to estimate the hydration of Bennu’s surface. We calculated the normalized optical path length (NOPL) as well as the effective single particle absorption thickness (ESPAT) [6, 7, 8] on Bennu’s hydration band and on the selected meteorite spectra. For both methods, we compare meteorite results with their H2O/OHH content, to estimate a H2O/OHH content of Bennu’s average surface. Carbonaceous chondrite meteorite H2O/OHH contents are derived from laboratory studies [9, 10].


Bennu spectra. Analysed spectra were acquired by OVIRS during Equatorial Station 3 (EQ3) of the Detailed Survey mission phase, on May 9, 2019, at 12:30 pm local solar time [11]. The reflectance spectra have been calibrated and photometrically corrected to an incidence angle of 0°, emission angle of 30°, and phase angle of 30°, using a McEwen photometrical model [12].

Meteorite spectra. We used three sets of meteorite absolute reflectance spectra, from [4,5], [8], and [13]. For each set, powdered meteorite sample spectra were measured under vacuum (asteroid-like conditions).

We selected over 40 meteorites for which bulk H values have been independently measured [9, 10]. In the case of Orgueil, Bells, and Tagish Lake, several samples were analysed and several H contents were ultimately derived [9, 14], all of which were used.


Normalized Optical Path Length (NOPL). The NOPL parameter was calculated as described in [6, 7, 8] on each meteorite spectrum, each individual Bennu reflectance spectrum, and the global average spectrum of Bennu. A linear continuum was fitted from 2.67 to 3.3 μm. The wavelength, at which the NOPL parameter is calculated, is the mean band minimum position for the EQ3 data set at 2.73 μm. Methods used to locate the band minimum are described in [3].

Effective Single Particle Absorption Thickness (ESPAT). The ESPAT parameter was calculated following the method of [6, 7, 8]. Absolute reflectance spectra of meteorites and Bennu’s surface were first converted into single-scattering albedo spectra [6, 7, 8]. A linear continuum was then fitted from 2.67 to 3.3 μm and the ESPAT parameter is calculated at 2.73 μm as well. Our analyses do not include the organic absorption bands, present longwards of ~3.3 μm [11, 15]. Thus, we compare NOPL and ESPAT results with the hydrogen content of H2O/OHgroups in hydrated phyllosilicates only, measured for the selected meteorites [9, 10].


Figure 1 shows the NOPL parameter variations across Bennu’s surface using EQ3 spectra.

Figure 1: Map of NOPL values computed at 2.73 μm for each EQ3 spectrum of Bennu.

We find a linear correlation (Figure 2) between the NOPL parameter calculated at 2.73 μm on meteorite spectra and the meteorite H2O/OHH content.

Using this linear correlation, for the NOPL calculated on Bennu’s EQ3 average spectrum, we estimate a H2O/OH H content for Bennu’s average surface of 0.54 ± 0.11 wt.%.


Figure 2: Linear correlation between NOPL calculated at 2.73 μm and H2O/OH H content of the seven selected meteorites (in colored points), and for average Bennu (blue circle).

As with the NOPL parameter, we also find a linear correlation between the ESPAT parameter calculated at 2.73 μm on meteorite spectra and the meteorite H2O/OHH content. We therefore estimate a H2O/OH H content for Bennu’s average surface of 0.49 ± 0.13 wt.%, using Bennu’s EQ3 mean ESPAT value and the latter correlation.


Discussion and Conclusion

The H2O/OH H content for Bennu’s average surface obtained using NOPL parameters is consistent with the range obtained with the ESPAT parameter. Both methods are based on estimating global H content (in H2O/OH groups of hydrated phyllosilicates) by analogy with meteorite data. The values of H2O/OH H content of Bennu’s average surface we obtained are 0.54 ± 0.11 and 0.49 ± 0.13 wt.% using the NOPL parameter and the ESPAT parameter, respectively. From our results (Figure 2), Bennu’s average surface is most similar to heated CMs and Tagish Lake. Both estimated H2O/OH H content ranges of Bennu’s average surface are more consistent with those of CM meteorites (0.46–1.36 wt%), Tagish Lake (0.50–0.69 wt.%), CR meteorites (0.30–1.20 wt.%), and CO meteorites (0.49–0.52 wt.%) [3, 9]. The gaussian modeling of the hydration band will complete those results.



This material is based on work supported by NASA under Contract NNM10AA11C issued through the New Frontiers Program. AP, MAB, FM, SF, PH and JDPD acknowledge funding support by CNES. INAF participation was supported by Italian Space Agency grant agreement n. 2017-37-H.0. We are grateful to the entire OSIRIS-REx Team for making the encounter with Bennu possible.



[1] Lauretta D. S. et al. (2017) Space Sci. Rev. 212, 925-984. [2] Reuter D. C. et al. (2018) Space Sci. Rev. 214, 54. [3] Hamilton V. E. et al. (2019) Nat. Astron. 3, 332. [4] Takir D. et al. (2013) Meteorit. Planet. Sci. 48, 1618–1637. [5] Takir D. et al. (2019) Icarus 333, 243–251. [6] Milliken R. E. et Mustard J. F. (2005) JGR, 110, E12001. [7] Milliken R. E. et al. (2007) JGR, 112, E08S07. [8] Garenne A. et al. (2016) Icarus, 264, 172-183. [9] Alexander C.M.O’D. et al. (2012) Science, 337, 721-723. [10] Alexander C.M.O’D. et al. (2013) Geochim. Cosmochim. Acta, 123, 244-260. [11] Simon et al. (in revision) Science. [12] Zou X.-D. et al., this meeting. [13] Potin S. et al. (2020) Icarus, 348, 113826. [14] Gilmour C. M. et al. (2019) Meteorit. Planet. Sci. 54, 1951–1972. [15] Kaplan et al. (2020) LPSC LI, 1050.

How to cite: Praet, A., Barucci, A., Kaplan, H., Merlin, F., Clark, B., Simon, A., Hamilton, V., Emery, J., Howell, E., Lim, L., Zou, X.-D., Reuter, D., Ferrone, S., Deshapriya, P., Fornasier, S., Hasselmann, P., Poggiali, G., Brucato, J., Takir, D., and Lauretta, D.: Hydration variations of Bennu’s surface: comparison of different methods. , Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-136,, 2020.

Xiao-Duan Zou, Jian-Yang Li, Beth Clark, Dathon Golish, Salvatore Ferrone, Amy Simon, Dennis Reuter, Deborah Domingue, Hannah Kaplan, Maria Barucci, Sonia Fornasier, Alice Praet, Pedro Hasselmann, Carina Bennett, Edward Cloutis, Eri Tatsumi, Daniella DelllaGiustina, and Dante Lauretta


NASA’s OSIRIS-REx (Origins, Spectral Interpretations, Resource Identification, and Security–Regolith Explorer) asteroid sample return mission (Lauretta et al., 2017) began operating in proximity to near-Earth asteroid (101955) Bennu in December 2018. Here we present an analysis of the global photometry of Bennu from measurements by the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS; Reuter et al., 2018). This instrument is a point spectrometer with a wedged filter design. OVIRS is used for the spectral characterization of the surface of Bennu, with a field of view of 4 mrad and an effective spectral range from 0.4 to 4.3 μm. Our work focuses on OVIRS data acquired from December 9, 2018, to September 26, 2019.


This study comprises the global observation data from Preliminary Survey and the two sub-phases of Detailed Survey, Baseball Diamond (BBD) and Equatorial Stations (EQ) (campaigns described in Lauretta et al., 2017). We use a total of 299,702 calibrated spots. More details about the data selection and calibration are introduced by Zou et al. (submitted).

3.Photometric analyses

We model the scattering properties of the surface of Bennu using the Lommel-Seeliger, Minnaert, McEwen, and Akimov photometric models. The best-fit model is a McEwen model with an exponential phase function and an exponential polynomial partition function. We use this model to correct the OVIRS spectra of Bennu to a standard reference viewing and illumination geometry at visible to infrared wavelengths for the purposes of global spectral mapping (Figure 1). We derive a bolometric Bond albedo map in which Bennu’s surface values range from 0.021 to 0.027. We find a phase reddening effect of 1.4±0.3 × 10−4 μm−1deg−1 across the wavelength range 0.48 to 2.5 μm, and our model is effective at removing this phase reddening.

We compare our OVIRS results to Golish et al. (2020)’s report on the global photometry of Bennu, based on imaging data from the OSIRIS-REx Camera Suite (OCAMS; Rizk et al., 2018). We also compare the results to ground observation and other minor planets including Ryugu.

Acknowledgements: This material is based upon work supported by NASA under Contract NNM10AA11C issued through the New Frontiers Program. We are grateful to the entire OSIRIS-REx Team for making the encounter with Bennu possible and the exploration highly successful. X.-D. Zou and J.-Y. Li also acknowledge partial support from the Solar System Exploration Research Virtual Institute 2016 (SSERVI16) Cooperative Agreement (Grant NNH16ZDA001N), SSERVI-TREX to the Planetary Science Institute. M. A. Barucci acknowledges funding support from CNES. 


Bennett, C.A., et al. 2020. A high-resolution global basemap of (101955) Bennu. Icarus. doi: 10.1016/j.icarus.2020.113690.

Ernst et al., 2018, The Small Body Mapping Tool (SBMT) for Accessing, Visualizing, and Analyzing Spacecraft Data in Three Dimensions, LPSC 49, abstract no. 1043.

Golish, D.R., et al. 2020. Disk-resolved photometric modeling and properties of asteroid (101955) Bennu. Icarus, doi:10.1016/j.icarus.2020.113724.

Lauretta, D.S., et al. 2017. OSIRIS-REx: sample return from asteroid (101955) Bennu. Space Science Reviews 212(1-2):925-984.

Reuter, D.C., et al. 2018. The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS): spectral maps of the asteroid Bennu. Space Science Reviews 214(2):54.

Rizk, B., et al. 2018. OCAMS: the OSIRIS-REx Camera Suite. Space Science Reviews 214(1):26.

Zou et al. (submitted). Photometry of asteroid (101955) Bennu with OVIRS on OSIRIS-REx. Icarus.

Figure 1. A global 3D facet-based map of the photometrically corrected (to 30°, 0°, 30°) OVIRS spots at a wavelength of 0.55 μm. The data are overlain on the OCAMS imaging basemap (Bennett et al., 2020), as viewed in the Small Body Mapping Tool (Ernst et al. 2018). Input spectra were obtained during Detailed Survey EQ3.

How to cite: Zou, X.-D., Li, J.-Y., Clark, B., Golish, D., Ferrone, S., Simon, A., Reuter, D., Domingue, D., Kaplan, H., Barucci, M., Fornasier, S., Praet, A., Hasselmann, P., Bennett, C., Cloutis, E., Tatsumi, E., DelllaGiustina, D., and Lauretta, D.: The Global Average Disk-Resolved Photometric Properties of (101955) Bennu, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-146,, 2020.

Sandra Potin, Sylvain Douté, Benoit Kugler, Florence Forbes, Pierre Beck, and Bernard Schmitt

Introduction: Reflectance spectroscopy is commonly used to retrieve chemical and physical properties of the Solar System bodies. However, the reflectance spectrum of a surface depends not only on its composition, but also on several parameters, including the viewing geometry [1]. Unlike laboratory measurements where the composition and texture of the sample and the geometry of measurement are fixed and controlled, observations of planetary bodies integrate spatial heterogeneities and changes of observation geometries due to both the shape and the topography of the surface. Here we compare two spectral photometric models to reproduce laboratory measurements of the Bidirectional Reflectance Distribution Functions (BRDFs) of a sample. We then employ the resulting models to calculate phase curves of the dwarf planet Vesta at different wavelengths and simulate spectral image cubes of its surface acquired with virtual telescopes.

Laboratory measurements: Bidirectional reflectance spectroscopy of a fine powder of howardite is acquired using the spectro-gonio radiometer SHADOWS [2], using the geometrical configurations presented in [1].

Inversion models: Inverting photometric models consists in estimating the best values of their parameters to reproduce the BRDFs sampled in the laboratory. It is then possible to generate the reflectance spectra that would have been measured under any geometrical configuration needed for the planetary simulation. We consider two photometric models: the Hapke model based on physical principles and a modified version of the Ross-Thick Li-Sparse model [3]. The model is able to recreate BRDFs of many natural surfaces and was recently modified by [4] to increase its performances. We use the following RTLSR form:


where the K terms play the roles of Lambertian (iso), volumetric (vol), geometric (geo), and forward scattering (fwd) components respectively. Spectral weights fiso , fvol , fgeo, and ffwd of the four components are determined by the surface reflectance properties. Note that the volumetric kernel contains a simplified treatment of the opposition effect with fixed width and amplitude as this effect is poorly constrained by our measurements. As for the inversion of the models, we consider a Bayesian framework using either a sampling approach based on Markov Chain Monte Carlo [5] or an inverse regression approach based on a preliminary learning step [6,7].

Results and comparison of the models: Both models, Hapke and RTLSR, accurately recreate the spectral features observed on the measurements (fig. 1)


Fig. 1: Bidirectional reflectance spectroscopy of the howardite measured in the laboratory (top left panel), and simulations based on inversion with the RTLSR model (bottom left panel) and with the Hapke model (bottom right model).

It has been shown previously [8] that the Hapke model can have difficulties when modeling the reflectance of chips that cannot be considered as loose agglomerates of grains. The parametric formulation of the RTLSR model can be a more robust descriptor of the BRDF but offers very limited physical interpretation. The sample of howardite used here consists of a fine powder with a narrow grain size distribution, thus the results found here are similar between the two models. We will thereafter use the RTLSR model, which has a slightly better SNR.

Application to surface: We then applied the RTLSR spectral BRDF on each facet of a shape model of Vesta. We create a simulated Vesta covered with the fine powder of howardite over the whole surface. We then simulate a serie of images taken during the fly-by of a spacecraft near this simulated object, with a fixed illumination source and a range of phase angle from 6° to 135°. Along with the varying phase angle due to the fly-by, the shape of the body and its surface topography induce local variations of the geometrical configuration (fig. 2), thus creating variations in the reflectance spectra.


Fig. 2: Left: Radiance image of Vesta at 400nm as observed with a representative imaging spectrometer. Right: Local emergence angles measured on each facet of the shape model of Vesta.

In the case of an unresolved observation, the reflectance spectrum of a body is integrated over its whole surface, thus averaging the reflectance at fixed phase angle over all geometrical variations due to the shape and topography. We then compare various spectroscopic and photometric phase curves of the simulated Vesta and the howardite sample measured in the laboratory as exemplified in fig. 3.


Fig. 3: Photometric phase curve of the simulated Vesta integrated over the whole surface (red solid line) compared to the BRDFs values measured in the laboratory (black circles) and inverted using the RTLSR model (blue circles).

The shape of the phase curve of the simulated Vesta shows a decreasing reflectance with increasing phase angle, typical of the micro and macroscale structures of the surface, drastically different from the phase curve measured in the laboratory. When comparing the spectroscopic parameters of the simulated Vesta to laboratory measurements acquired in the typical geometry of i0° e30°, we show that in the laboratory the reflectance is underestimated, while the spectral slope and amplitude of the absorption features are overestimated. As the composition of the surface of the simulated body is fully homogeneous, these differences are only due to the shape and topography of the asteroid.

Conclusion: We show here and application of a laboratory measured BRDF for the simulation of spectral image cubes and phase curves of a planetary body. If the observed body is not fully resolved, as it is often the case for asteroids spectroscopy, the measured reflectance spectrum is averaged over the whole surface. The observed spectra can be different from those measured in the laboratory, only because of geometrical effects due to the shape and topography of the surface.

References: [1] S. Potin et al. (2019) Icarus, 333, 415-428. [2] S. Potin et al. (2018) Applied Optics, 57, 8279-8296. [3] W. Lucht et al. (2000), IEEE Trans. Geosci. Remote Sens., 38, 977-998. [4] Z. Jiao et al. (2019) Remote Sens. Env., 221, 198-209. [5] F. Schmidt et al. (2015), Icarus, 260, 73-93. [6] A. Deleforge et al. (2014), Stat. & Comp. [7] B. Kugler et al. (2020), this conference [8] S. Potin et al. (2020), 51th LPSC, 1874.

How to cite: Potin, S., Douté, S., Kugler, B., Forbes, F., Beck, P., and Schmitt, B.: Simulated phase curves of Vesta based on laboratory bidirectional reflectance spectroscopy, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-201,, 2020.

Marcel Popescu, Julia de León, Carlos de la Fuente Marcos, Ovidiu Văduvescu, Raúl de la Fuente Marcos, Javier Licandro, Viktoria Pinter, Eri Tatsumi, and Lucian Curelaru

Recent numerical simulations (Greenstreet et al. 2012, Granvik et al. 2018) predicted the existence of a population of small bodies that is orbiting entirely inside Venus orbit. They could represent about 0.22 % of the steady-state near-Earth asteroids (NEAs). These asteroids are called Vatiras (by analog with the Atira-class NEAs) or Interior to Venus Orbit Objects. However, only at the beginning of this year (January 4, 2020) the first one was discovered by Zwicky Transient Facility (Bolin et al. 2020). It is called 2020 AV2 and has the aphelion at 0.654 AU, and the perihelion at 0.457 AU.

The dynamical history of this object has been explored using N-body simulations (de la Fuente Marcos & de la Fuente Marcos 2020). It has been shown that 2020 AV2 was a former Atira-class, and perhaps a former Aten-class asteroid, which reached the Vatira orbit relatively recently in astronomical terms, ∼105 yr (within 9σ confidence level). Similar results have also been reported by Greenstreet (2020).

The orbit of 2020 AV2 makes it a peculiar case compared with those of the near-Earth asteroids. It is subjected to high temperature, strong solar wind irradiation, and close approaches to Mercury and (more distant) Venus. In this context, we carried out an observing run aimed at obtaining spectroscopic and photometric data for 2020 AV2. We used the 2.56 m Nordic Optical Telescope (NOT) and 4.2m William Herschel Telescope (WHT), both located at El Roque de los Muchachos Observatory in La Palma, Canary Islands (Spain). The observations were performed on the evenings of January 11, 13, and 14, 2020. They were challenging due to the low maximum elongation of this target (about 37 deg during our observations).

We obtained two visible spectra with the ACAM/WHT and with the ALFOSC/NOT instruments. The near-infrared part of the spectrum was obtained with the LIRIS/WHT instrument using the lr-zj prism. It covers the 0.9-1.5 μm wavelength range. The merged spectrum is shown in Fig. 1.

Fig.1 The combined visible - near infrared spectrum of 2020 AV2 (Popescu et al. 2020). The spectral curve is normalized to unity at 1.25 μm. The reflectance maximum at 0.745 μm and the band minimum at 1.075 μm are outlined by the two markers.

The merged spectrum, covering a wavelength interval between 0.5 and 1.5 μm, allowed us to classify 2020 AV2 as an Sa-type asteroid. The value estimated for the 1 μm band center, BIC = 1.08 ± 0.020 μm, points towards a composition similar to that of the S(I) subtype of asteroids with olivine-pyroxene mixtures, defined by Gaffey et al. (1993). This value of BIC is indicative of a ferroan olivine mineralogy similar to that of brachinite meteorites.

Last but no least, we derived the effective diameter of this Vatira to be 1.50+1.10−0.65 km by considering the average albedo of A-type and S-complex asteroids (pV = 0.23−0.08+0.11), and the absolute magnitude (H=16.40 ± 0.78 mag).

References: [1] Bolin et al. 2020, MPEC 2020-A99; [2] de la Fuente Marcos & de la Fuente Marcos,  2020, MNRAS, 494, L6; [3] Granvik et al. 2018, Icarus, 312, 181; [4] Greenstreet et al. 2012, Icarus, 217, 355; [5] Greenstreet 2020, MNRAS, 493, L129; [6] Popescu et al. 2020, MNRAS, paper accepted,

Acknowledgements: This work was developed in the framework of EURONEAR collaboration and of ESA P3NEOI projects. The work of M.P. was supported by a grant of the Romanian National Authority for Scientific Research - UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0371. M.P., J.dL. and J.L. acknowledge support from the AYA2015-67772-R (MINECO, Spain), and from the European Union's Horizon 2020 research and innovation programme under grant agreement No 870403 (project NEOROCKS).This work was partially supported by the Spanish MINECO under grant ESP2017-87813-R. Based on observations made with the William Herschel Telescope (operated by the Isaac Newton Group of Telescopes) and the Nordic Optical Telescope. The paper make use of data published by the following web-sites Minor Planet Center, JPL Small-Body Database Browser , SMASS - Planetary Spectroscopy at MIT.

How to cite: Popescu, M., de León, J., de la Fuente Marcos, C., Văduvescu, O., de la Fuente Marcos, R., Licandro, J., Pinter, V., Tatsumi, E., and Curelaru, L.: Spectral properties of 2020 AV2, the first known asteroid orbiting inside Venus orbit, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-549,, 2020.

Vanessa Lowry, Kerri Donaldson Hanna, Humberto Campins, Neil Bowles, and Victoria Hamilton

Linear least squares unmixing of infrared spectra is a fast and effective way to spectrally estimate the modal mineral abundances of laboratory samples and remotely-sensed surfaces to within 5% on average [e.g., 1]. This technique has been applied to spectra of whole rocks, coarse particulates, meteorites, and the Martian surface to successfully determine modal abundances [e.g., 1-3]. With the recent arrival of NASA’s OSIRIS-REx spacecraft to asteroid 101955 Bennu, the OSIRIS-REx Thermal Emission Spectrometer (OTES) has been providing a wealth of data to interpret using spectral unmixing techniques [e.g., 4].

The assumption of linear spectral unmixing allows for the deconvolution of a mixed spectrum if the individual spectra and particle sizes of the pure end members are present within a spectral library. By implementing a weighted linear least squares (WLS) unmixing algorithm, one is able to deconvolve these mixed spectra into areal percentages of each endmember with the underlying assumption that this then corresponds to the volume percentages [e.g., 1-3]. At thermal infrared (TIR) wavelengths, end member spectra of coarse particulates combine linearly due to high absorption coefficients and relatively small mean optical paths, which limits most of the volumetric scattering [e.g., 1-3]. Linearity in the TIR region continues as particle size decreases until the wavelength of light approaches the particle size, at this point particles become optically thin and non-linear behavior (e.g., volumetric scattering) is observed. However, Ramsey and Christensen [1] demonstrated that when unmixing fine particulates (10 – 20 μm) with a spectral library of end members at the same particle size linear unmixing can still be used to estimate modal mineral abundances.

In this study we investigate the effectiveness of a linear least squares unmixing approach to estimate mineral abundances for samples dominated by fine particulates (< 38 μm). We use a WLS algorithm and a spectral library of fine particulate pure minerals to unmix spectra of a suite of fine particulate, primitive asteroid analogs. Results from this investigation have implications for the interpretation of spectral observations of primitive asteroids that have a layer of fine particulate regolith.

[1] Ramsey M. S. and Christensen P. R. (1998) JGR, 103, 577-596. [2] Hamilton V. E. and Christensen P. R. (2000) JGR, 105, 9717-9733. [3] Rogers A. D. and Aharonson O. (2008) JGR, 113,EO6S14. [4] Hamilton V. E. et al. (2019) Nature Astron., 3, 332-340.

How to cite: Lowry, V., Donaldson Hanna, K., Campins, H., Bowles, N., and Hamilton, V.: Linear unmixing of fine particulate materials: implications for compositional analyses of primitive asteroids, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-400,, 2020.

Matthew Richardson, Andrew Rivkin, and Amanda Sickafoose


As part of a larger study to elucidate the presence of hydrated minerals on asteroid surfaces, we are developing a robust taxonomic classification system using spectroscopic observations in the vicinity of 3 μm. We have constructed a Python algorithm to identify band centers and band depths near 3 µm for a set of normalized, thermally-corrected asteroid spectra for use to serve as inputs to Python’s Scikit-Learn library of Machine Learning (ML) algorithms. We anticipate a thorough investigation of both Principal Component Analysis and ML (supervised, unsupervised, and Artificial Neural Network) techniques to assess which technique is likely to be better suited for classifying the 3-µm data. At this writing, we have run tests using Python’s Agglomerative clustering ML algorithm to examine possible clustering scenarios. These initial steps have given us some familiarity with the mechanics of using ML on the 3-µm dataset as well as serving to identify some possible pitfalls or cul-de-sacs. Presented here are the preliminary results we have obtained.


Although various techniques have been used, asteroid classification has typically been done via Principal Component Analysis (PCA: [1,2]). PCA is a statistical technique that reduces the dimensionality of a dataset by identifying the most important parameters within a dataset based on their variance. Parameters that exhibit the greatest amount of variance are considered to be of greater importance while parameters with the least amount of variance are considered to be of lower importance. While the PCA technique produces better visualizations of the data by reducing the dimensionality of a dataset, the PCA technique comes with some drawbacks. Disadvantages such as its dependence on scale and information loss due to the orthogonal property of PCA can cause interpretation of PCA results to prove to be a more critical and time-consuming process. Therefore, exploring other means of classification may prove to be worthwhile.

Machine Learning (ML) algorithms have had a significant impact on the way in which data is analyzed and interpreted, and have already proven to be a powerfully reliable resource in the field of planetary science. Accordingly, the application of ML to an asteroid taxonomy has the potential to be more efficient, objective, and easy-to-implement than PCA. ML algorithms can be supervised, in which the program “learns” from training data and is able to classify new inputs, or unsupervised, in which the program analyzes the dataset to determine patterns such as clusters. [3] used an Artificial Neural Network (ANN, a subset of ML) to classify asteroids, work followed up by [4]. Recent explorations of supervised ML for asteroid taxonomy are promising, and have applied training sets from existing databases to new visible and/or NIR photometric data (e.g. [5,6,7]).

We seek to explore the benefits of ML algorithms, as well as compare and contrast to the PCA technique, in the production of an asteroid taxonomy. Our initial exploration has utilized a set of normalized, thermally-corrected asteroid spectra in the vicinity of 3 µm. We have identified band centers and band depths and served this parameter space as inputs to Python’s Agglomerative clustering ML algorithm.


Thermal corrections of the asteroid spectra were performed via a forward model that uses a modified version of the Standard Thermal Model (STM: [8]). The forward model treats the beaming parameter as a free parameter adjusting its value for each iteration of the STM until it converges onto a value that yields expected long-wavelength continuum behavior. Spectra were then normalized to unity at a wavelength of 2.3 µm, followed by identification of band centers and band depths near 3 µm using both polynomial and Gaussian fits. In addition, band depths were measured at wavelengths of 2.9 µm and 3.2 µm to gather more information on asteroid band shapes. Lastly, the aforementioned calculated spectral features were input into Python’s Agglomerative clustering algorithm to determine which asteroid spectra shared similar features.


As part of a larger investigation to better understand hydrated mineralogies as they apply to asteroids, we have begun work towards developing a quantitative taxonomic framework derived from asteroid spectra in the wavelength range from 2.0-4.0 µm. Our exploration thus far of Python’s Agglomerative clustering algorithm has proven to be fruitful. Minor changes to the parameterization of this algorithm can yield very different results, which naturally can lead to different interpretations. The Agglomerative clustering algorithm is one of many the powerful ML algorithms we will explore against the PCA technique, all of which we will be discussing in our presentation.


[1] Bus, S. J. and Binzel, R. P. (2002). Phase II of the Small Main-Belt Asteroid Spectroscopic Survey: A Feature-Based Taxonomy. Icarus, 158:146-177.

[2] DeMeo, F. E., Binzel, R. P., Slivan, S. M., and Bus, S. J. (2009). An extension of the Bus asteroid taxonomy into the near-infrared. Icarus, 202:160-180.

[3] Howell, E. S., Merenyi, E., and Lebofsky, L. A. (1994). Classification of asteroid spectra using a neural network. 99:10847-10865.

[4] Merenyi, E., Howell E. S., Rivkin A. S., and Lebofsky L. A. (1997). Prediction of water in asteroids from spectral data shortward of 3 µm. Icarus, 129:421-439.

[5] Moment, M., Trilling, D. E., Borth, D., Jedicke, R., Butler, N., Reyes-Ruiz, M., Pichardo, B., Peterson, E., Axelrod, T., and Moskovitz, N. (2016). First results from the rapid-response spectrophotometric characterization of near-earth objects using UKIRT. The Astronomical Journal, 151:98

[6] Popescu, Marcel & Licandro, Javier & Carvano, Jmf & Stoicescu, R. & De León, Julia & Morate, David & Boac\u{a, I.L. & Cristescu, C.. (2018). Taxonomic classification of asteroids based on MOVIS near-infrared colors. Astronomy & Astrophysics. 617. 10.1051/0004-6361/201833023.

[7] Wallace, S. M., Burbine, T. H., Sheldon, D., Dyar, M. D. (2019). Machine Learning applied to asteroid taxonomy based on reflectance spectroscopy: An objective method. LPSC 50, Abstract # 1097.

[8] Lebofsky, L. A., Sykes, M. V., Tedesco, E. F., Veeder, G. J., Matson, D. L., Brown, R. H., Gradie, J. C., Feierberg, M. A., Rudy, R. J. (1986). A refined standard thermal model for asteroids based on observations of 1 Ceres and 2 Palas. Icarus. 68:239-251.

How to cite: Richardson, M., Rivkin, A., and Sickafoose, A.: Investigating Machine Learning as a Basis for Asteroid Taxnomies in the 3-Micron Spectral Region, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-963,, 2020.

Bernard Schmitt, Philippe Bollard, Damien Albert, Lydie Bonal, and Olivier Poch and the SSHADE Consortium

The SSHADE database infrastructure ( hosts spectral data of many different types of solids: ices, snows, minerals, carbonaceous matters, meteorites, IDPs and other cosmo-materials,… covering a wide range of wavelengths: from X-rays to millimeter wavelengths.

Its Search / Visualization / Export interface is open to the community since February 2018. It currently contains over 3500 spectra.

We are currently developping a 'band list' database that will provide the band parameters (position, width, intensity, isotopic  species, attribution mode, ...) of a number of solids of astrophysical and planetary interest in various phases (crystalline, amorphous, ...) and mixtures (different compositions) and at different temperatures or pressures. We will first feed this database from critical compilations of all data published in various journals for pure ices and molecular solids (hydrates, clathrates, sulfur compounds, ...) and their mixture, including the own works of the members of the SSHADE consortium.

An efficient search tool will allow to filter on various parameters such as band position (or spectral range) and intensity, expected molecular or atomic formula, type of vibration (e.g. infrared, Raman, fluorescence...) and display the results graphically. A demonstrator prototype is online in the 'old GhoSST' database (

This band list will be a key tool for astronomers and space explorers to identify unknown absorption bands observed in the spectra of the surface or atmosphere of many objects in the solar system. Once the best candidate solid selected by the user the tool will point to the best relevant spectral data present in the SSHADE databases.



How to cite: Schmitt, B., Bollard, P., Albert, D., Bonal, L., and Poch, O. and the SSHADE Consortium: SSHADE, its solid spectra databases and its future band list of molecular solids, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-644,, 2020.

Benoit Kugler, Florence Forbes, and Sylvain Douté


When studying the Martian surface, the composition of the materials is established on the basis of spectral detection, unmixing, and physical modelling using images produced by hyperspectral cameras. Information on the microtexture of surface materials such as grain size, shape, roughness and internal structure can also be used as tracers of geological processes (Fernando et al. PSS 2016). This information is accessible under certain conditions thanks to hyperspectral image sequences acquired over a site of interest from eleven different angles by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM@MRO, Murchie et al. JGR-Planets 2009) . Similar multi-angular observations can also be realized in the laboratory with spectro-photo-goniometers, on planetary analog materials or real extraterrestrial matter (Potin et al. Icarus 2019) . In both cases, the interpretation of the surface Bidirectional Reflectance Distribution Factor extracted from these observations, in terms of composition and microtexture, is based on the inversion of the Hapke model, a semi-empirical photometric model that relates physically meaningful parameters x to the reflectivity y of a granular material for a given geometry of illumination and viewing (Schmidt and Fernando Icarus 2015).
This work presents an efficient method, based on a learning approach in a Bayesian framework, able to invert the Hapke model on large experimental or remote sensing datasets, as illustrated on a challenging CRISM multi-angular sequence of images. 

Inversion characteristics

In the CRISM case, the dataset provides both spatial and spectral dimensions. More specifically, each of the Nspatial spatial point of the scene is observed at Nspectral wavelengths, yielding Nspatial× Nspectral vectors of reflectance values. This high number of observations to be inverted usually makes approaches like Markov Chain Monte-Carlo simulations unacceptably slow. The approach we propose starts with a computer intensive learning phase that builds a statistical model for the Hapke model; it is then applied for all observables yobs.
Each observable is a vector of D reflectances, one for each geometry of measure. In the case of CRISM data, D = 11 is moderate, but in laboratory, D may be one order of magnitude higher. The statistical model we propose has the advantage to be able to handle high dimensional settings while remaining computationally tractable.
The Bayesian framework offers a natural solution to propagate uncertainties on measures, which are formalized as variance on the prior distribution, while the variance of the posterior distribution assesses the uncertainty induced by the inversion.
The complexity of the forward radiative transfer model often makes our inverse problem ill-posed. In practice, multiples solutions might be acceptable. More than a point estimator, our statistical model provides a full posterior density, whose multi-modality can be assessed and interpreted.

2 Bayesian inversion via a learning approach

From a statistical point of view, we formalize our model as couple of random variables (X,Y), where Y = F(X) + ε. X has a prior distribution on the physical parameters space, F is the forward model and ε is a centered Gaussian noise accounting for measure uncertainties. The main idea is to use a two steps approach: first, approximate this model by a parametric surrogate model, second use the surrogate model to invert the observations.
We chose the family of the so-called Gaussian Locally-Linear Mapping (GLLiM) (Deleforge et al StatComp 2015) . Such models are expressive enough to approximate highly non-linear, complex, forward models, while remaining tractable.
The learning step is performed by generating a training dictionary composed of samples from (X,Y), on which the likelihood is maximized by a standard EM algorithm. Regarding the dimensionality of the problem, the number of parameters is proportional to D, making this model suitable even for high dimensional observables. Moreover, the computational cost of the learning phase does not scale with the number of observations to be inverted.
The second step is performed by computing the posterior distribution of the random variable X given yobs. The inverse of the surrogate GLLiM has an explicit formulation in the form of a Gaussian Mixture. From this posterior density, the mean and variance are straightforward to compute. To handle a multi-solution scenario, the mixture can be further exploited.

Massive inversion of spectro-images

The dataset comes from a multi-angular observation of the South Pole of Mars by CRISM. The targeted scene presents spatially segregated C02 ice, H2O frost, and mineral dust (Douté and Pilorget EPSC 2017). After fusion and atmospheric correction of eleven hyperspectral images (Ceamanos et al. JGR-Planets 2013), the dataset provides both spatial and spectral dimensions, totaling Nobs = 154650 = 3093 × 50 measurements vectors yobs, which makes MCMC approach unacceptably slow. Maps of Hapke’s parameter values are generated from the results of our inversion and superposed with transparency onto the full resolution CRISM nadir image, which serves as a geological control background image.
The results are satisfying from the application point of view. The colour composition of Figure 1 reflects the variation of ω at three wavelengths and corresponds well with the spatial distribution of the three previous materials and their known spectral optical properties. The map of the roughness parameter θ averaged over the spectral dimension (Figure 2) is color coded by intervals of values whose spatial variations are correlated with the composition and the structures of the terrains. In general, we find that all predicted parameters preserve some spatial regularity and show meaningful correlations with the composition and the geology.