Europlanet Science Congress 2021
Virtual meeting
13 – 24 September 2021
Europlanet Science Congress 2021
Virtual meeting
13 September – 24 September 2021
Mercury Science and Observations


Mercury Science and Observations
Conveners: Johannes Benkhoff, Léa Griton | Co-conveners: Alice Lucchetti, Go Murakami, Joana S. Oliveira, Beatriz Sanchez-Cano, Joe Zender
Thu, 23 Sep, 10:40–11:25 (CEST)

Session assets

Discussion on Slack

Oral and Poster presentations and abstracts

Chairpersons: Léa Griton, Joana S. Oliveira, Alice Lucchetti
Mercury Environment
Matthew James, Timothy Yeoman, Suzanne Imber, Jim Raines, and Ryan Dewey


The entire dataset of 1-minute proton spectra provided by MESSENGER’s Fast Imaging Plasma Spectrometer (FIPS [1]) are each numerically fitted with a κ distribution function in order to provide estimates of proton density, n, and temperature, T , in the vicinity of Mercury’s magnetosphere. A collection of artificial neural networks (ANNs) are trained to assess the quality of the newly fitted distribution functions, with a final classification accuracy of 96%. Of the 223,282 spectra, ∼160,000 were classified as having “good” fitting κ distributions, ∼133,000 of which were measurements obtained from within the magnetosphere, and ∼18,000 were from the magnetosheath. The “good” magnetospheric densities and temperatures were subsequently used to create a 3D ANN-based model of the Hermean magnetosphere.

1. Introduction

Mass loading is an important factor in controlling the timescales over which processes such as wave propagation and reconnection can occur within a magnetized plasma. The speed at which magnetohydrodynamic (MHD) waves propagate is characterized by the local Alfvén velocity, vA , where vA= , B is themagnetic field magnitude, ρ is the plasma mass density and μ0 is the permeability of free space. Fundamentally, the higher the local plasma mass density is–the slower waves propagate. Similarly, the beta of a plasma (β) depends on the density and temperature where β= and kB is the Boltzmann constant.

Magnetic reconnection between two adjacent magnetized plasmas is affected by the difference in plasma beta (∆β) between the two fluids, where a larger ∆β suppresses reconnection and thus a larger shear angle is required for reconnection to occur [2, 3]; conversely, low ∆β makes reconnection more favorable with small shear angles [3, 4].
The Fast Imaging Plasma Spectrometer (FIPS [1]) on board the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER [5]) spacecraft operated at Mercury for approximately 4 years (from March 2011 to April 2015) during the orbital phase of the mission. During this time, it sampled the ions from both within and outside of the Hermean magnetosphere, providing at least 1.25 million proton spectra every 10 or 60 s, depending upon the instrument mode. Many of those were burst-mode spectra and were combined in groups of 6 to form 1-minute average spectra, resulting in ∼220,000 60 s proton spectra.

In the work presented here, the entire dataset of 1-minute FIPS spectra are each numerically fitted with κ distribution functions, providing improved estimates of proton density and temperature over previous Maxwellian fits. The quality of the distribution function fits are assessed using artificial neural networks, resulting in a reduced dataset of more reliable plasma moments. Finally, new average and scalable 3D models of magnetospheric protons are presented using the new moments.

2. Fitting the Spectra

The κ-distribution function, fκ [6],

where Γ(x) is the Gamma function, was fitted numerically to the FIPS spectra using the downhill-simplex method [7]. This distribution function can be used to describe both plasmas in thermal equilibrium (i.e. Maxwellian, κ → ∞) and suprathermal plasmas (κ < 10).

Figure 1 shows an example of the κ distribution fit (pink) to a FIPS spectrum, compared to a Maxwellian fit (gray). The κ function fits well at all velocities in the FIPS spectrum, while the Maxwellian deviates significantly at higher energies.

3. Assessing Quality of Spectral Fit

Determining whether the κ distribution fit is a simple task to perform by eye, but would be a time consuming task to do for all 223,282 spectra, with potentially inconsistent results. One option would be to use a simple measure of goodness of fit (e.g. χ2 ), but this does not work very well with these data - so classification neural networks were used instead.
Initially, spectra were split into 8 equal parts, each of which was analyzed by one of 8 neural networks (see figure 2) – providing a probability that each section had a “good” distribution function fit. The 8 probabilities formed part of the input to the final ANN, along with other parameters such as ∆f (difference between the spectral fit and the data), which produced an overall probability that the spectral fit was reliable.

4. 3D Proton Model

Figure 3 shows an overview of the new magnetospheric proton densities and temperatures in the equatorial plane and in the X-Z plane. The proton density appears to be lowest in the closed regions of the magnetosphere, and highest in the cusp, whereas the temperatures exhibit the inverse of this trend.

Using the new density and temperature measurements as training data, and assuming that there is some hemispherical symmetry, a new 3D model was created using an ANN (see figure 4 a and b). The model was trained simply to map a Cartesian position vector as an input to density and temperature. The model may also be combined with the KT17 magnetic field model [8] to provide estimates of the upper limit to the Alfvén speed (figure 4c) and the proton contribution to plasma β (figure 4d). A scalable model was also produced, where the magnetopause standoff distance was used as an extra ANN input.


The work by M.K.J., T.K.Y., S.M.I and E.J.B is supported by STFC grant ST/H002480/1.

MESSENGER project is supported by contracts NASW-00002 and NAS5-97271.


[1] Andrews, et al. (2007), The Energetic Particle and Plasma Spectrometer Instrument on the MESSENGER Spacecraft, Space Sci. Rev., 131(1-4), 523–556, doi:10.1007/s11214-007-9272-5.

[2] Swisdak et al. (2010), The vector direction of the interstellar magnetic field outside the heliosphere, The Astrophysical Journal, 710(2), 1769–1775, doi:10.1088/0004-637x/710/2/1769.

[3] Phanet al. (2016), Messenger observations of cusp plasma filaments at mercury, Journal of Geophysical Research: Space Physics, 121(9), 8260–8285, doi:10.1002/2016JA022552.

[5] Solomon et al. (2007), Messenger mission overview, Space Sci. Rev., 131(1), 3–39, doi:10.1007/s11214-007-9247-6.

[6] Pierrard and Lazar (2010), Kappa Distributions: Theory and Applications in Space Plasmas, Solar Physics, 267(1), 153–174, doi:10.1007/s11207-010-9640-2.

[7] Nelder and Mead (1965), A Simplex Method for Function Minimization, Comput. J., 7(4), 308–313, doi:10.1093/comjnl/7.4.308.

[8] Korth et al. (2017), A Dynamic Model of Mercury’s Magnetospheric Magnetic Field, Geophys. Res. Lett., 44(20), 10,147–10,154, doi:10.1002/2017GL074699.

How to cite: James, M., Yeoman, T., Imber, S., Raines, J., and Dewey, R.: A machine learning approach to classifying MESSENGER FIPS proton spectra, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-611,, 2021.

Federico Lavorenti, Pierre Henri, Francesco Califano, Sae Aizawa, and Nicolas Andre

Density inhomogeneities are ubiquitous in space and astrophysical plasmas, in particular at contact boundaries between different media. They often correspond to regions that exhibits strong dynamics on a wide range of spatial and temporal scales. Indeed, density inhomogeneities are a source of free energy that can drive various plasma instabilities such as, for instance, the lower-hybrid-drift instability which in turn transfers energy to the particles through wave-particle interactions and eventually heats the plasma. Here, we address the role of this instability in the Hermean plasma environment were kinetic processes of this fashion are expected to be crucial in the plasma dynamics and have so far eluded the measurements of past missions (Mariner-X and MESSENGER) to Mercury.
The goal of our work is to quantify the efficiency of the lower-hybrid-drift instability to accelerate and/or heat electrons parallel to the ambient magnetic field.
To reach this goal, we combine two complementary methods: full-kinetic and quasilinear models.
We report self-consistent evidence of electron acceleration driven by the development of the lower-hybrid-drift instability using 3D-3V full-kinetic numerical simulations. The efficiency of the observed acceleration cannot be explained by standard quasilinear theory. For this reason, we develop an extended quasilinear model able to quantitatively predict the interaction between lower-hybrid fluctuations and electrons on long time scales, now in agreement with full-kinetic simulations results. Finally, we apply this new, extended quasilinear model to a specific inhomogeneous space plasma boundary: the magnetopause of Mercury, and we discuss our quantitative predictions of electron acceleration in support to future BepiColombo observations.

How to cite: Lavorenti, F., Henri, P., Califano, F., Aizawa, S., and Andre, N.: Electron acceleration by the lower-hybrid-drift instability at Mercury: an extended quasilinear model, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-10,, 2021.

Sophia Zomerdijk-Russell, Adam Masters, and Daniel Heyner

Mercury’s magnetosphere is a unique and dynamic system, primarily due to the proximity of the planet to the Sun and its small size. Interactions between solar wind and embedded Interplanetary Magnetic Field (IMF) and the dayside Hermean magnetosphere drive an electric current on the system’s magnetopause boundary. So far, electromagnetic induction due to magnetopause motion in response to changing external pressure has been used to constrain Mercury’s iron core size. Here we assess the impact a changing IMF direction has on the Hermean magnetopause currents, and the resulting inducing magnetic field. Observations made by MESSENGER during subsolar magnetopause boundary crossings in the first ‘hot season’, are used to demonstrate the importance of the IMF direction to Mercury’s magnetopause currents. Our 16 boundary crossings show that introduction of external IMFs change the magnetopause current direction by 10° to 100°, compared to the case where only the internal planetary field is considered. Analytical modelling was used to fill in the bigger picture and suggests for an east-west reversal of the IMF, typical of the heliospheric current sheet sweeping over Mercury’s magnetosphere, the inducing field at Mercury’s surface caused by the resulting magnetopause current dynamics is on the order of 10% of the global planetary field. These results suggest that IMF variability alone has an appreciable effect on Mercury’s magnetopause current and generates a significant inducing magnetic field around the planet. The arrival of the BepiColombo mission will allow this response to be further explored as a method of probing Mercury’s interior.

How to cite: Zomerdijk-Russell, S., Masters, A., and Heyner, D.: Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury’s interior, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-103,, 2021.

Mercury Surface
Valentina Galluzzi, Joana S. Oliveira, Jack Wright, David A. Rothery, and Lon L. Hood

In the last months of its mission, MESSENGER was able to obtain measurements at low altitude (< 120 km). This has made it possible to measure small magnetic field signals, probably of crustal origin (Johnson et al, 2015). Maps of the crust signatures at 40 km altitude were produced by Hood (2016) and Hood et al. (2018), showing that the strongest anomalies are about 9 nT in the Caloris basin. Some of the anomalies are associated with impact craters, and it has been demonstrated that this is not a coincidence (Hood et al., 2018). It is believed that these anomalies are the result of impactor materials rich in magnetic carriers (e.g., metallic iron) that were incorporated on the surface acquiring remanent magnetic fields during the cooling of the material. We analyzed whether the anomalies of the crustal field are related to geological characteristics by examining two Hermean craters in order to test this impactor hypothesis. Anomalies associated with Rustaveli and Stieglitz craters are slightly or totally asymmetric with respect to the crater center. The morphology and geological setting of these two fresh impact craters that still maintain a well-preserved ejecta blanket and visible secondary crater chains are investigated to constrain the overall impact dynamics. In both cases, slight asymmetries in the morphology and ejecta distribution show that the magnetic anomalies correlate well with the location of impact melt. Rustaveli is associated with a ~5 nT crustal magnetic anomaly centered close to the crater’s midpoint, although offset ~20 km east-southeast. This offset is somewhat consistent with the downrange direction implied by Rustaveli’s impact melt and crater chains distribution. For Stieglitz, an anomaly larger than 3 nT includes most of the ejecta melt locations towards southwest. The ejecta melt cluster to the north of the crater corresponds to an anomaly of ~5 nT, while the largest anomaly of ~7 nT is found further north and closely corresponds to the crater’s deepest chain. For both craters, the melt likely recorded the prevailing magnetic field of Mercury after quenching. Hence, both impactors brought magnetic carriers to the surface that could record the past magnetic field of Mercury. Acknowledgments: The authors gratefully acknowledge funding from the Italian Space Agency (ASI) under ASI-INAF agreement 2017-47-H.0 and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 776276.

Hood, J. Geophys. Res. Planets 121, 2016;
Hood et al., J. Geophys. Res. Planets 123, 2018;
Johnson et al., Science 348, 2015.

How to cite: Galluzzi, V., Oliveira, J. S., Wright, J., Rothery, D. A., and Hood, L. L.: Young craters of Mercury correlating with offset magnetic anomalies, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-699,, 2021.

Sébastien Besse, Claudio Munoz, and Thomas Cornet

Introduction: The MESSENGER spacecraft has returned a wealth of data from Mercury that offers infinite research opportunities. The objective of our modest science team, the Mercury Surface Spectroscopy (MeSS) group, is to enable and maximise the science exploitation of the visible to near-infrared spectrometer (MASCS) onboard the MESSENGER mission to unravel Mercury’s surface.

The MeSS project is currently collecting research investigations of Mercury’s surface through data mining, knowing that the output of this work is of paramount importance for the preparation of BepiColombo’s exploration of Mercury.

Scientific topics for Mercury’s surface: In preparation for the next Decadal Survey, the Mercury science community defined in 2019 a list of high priority science at Mercury. The MeSS approach is supporting many of those high priority questions, and in particular those related to surface analysis. Are geological processes (e.g. formation of hollows and scarps) actively taking place today? How and why has volcanism changed so dramatically through time? To what degree have secondary processes affected the surface composition and mineralogy of Mercury? With the use of the MeSS, scientific users can explore those science questions with the MASCS observations.

The MeSS infrastructure: The MeSS database is a PostgreSQL relational database. This MeSS database contains the entire set of observations acquired by MASCS (i.e. >4.000.000 footprints) with all the relevant metadata to evaluate the MASCS data. Additionally, the MeSS database provide access to a product that combine the UV-visible and near channels of the spectrometer following the approach described in [1]. A view of the various parameters available in the MeSS database is presented in Fig 1. A python interface is available to provide query access to the database and explore the products of interest based on various criteria. Spectral parameters defined in [2] are also added as a table in the MeSS database. With the various information provided in the MeSS database, users can explore a combination of parameters of interest to explore the properties of Mercury’s surface.

Fig 1: Illustration of the various parameters and their relationships in the MeSS


Fig 2: Output of a query in the MeSS Database for the footprints with an incidence angle <75, a detector temperature <40, the visible slope as calculated in [3], and around the Caloris basin.

Results: After few years of development, and despite a slow down due to the pandemic, the MeSS database has been used to explore Mercury’s surface, and provide scientific results. [2,4,5] made use of the MeSS database to explore the mineralogical and physical properties of volcanic landforms. Our results have in particular highlighted the size of explosive volcanism and the asymmetry of some specific deposits. Hollows are small depressions on the surface potentially created by degassing of the sub-surface, and our latest analysis using spectroscopic data favour scarp-retreat as one of the mechanisms explaining the growth of hollows [3].

New collaborations are underway to investigate more scientific topics through the MeSS (space weathering, impact features, spectral properties of magnetic anomalies, etc.).

Future and collaborations: The content of the MeSS Database is rapidly evolving:

  • New parameters are and can be added. In particular, the MeSS team is currently adding the spectral curvature calculated in [3].
  • New datasets are always of interest to provide context to point spectrometers. The MeSS team is exploring the addition of MDIS and MLA data. 
  • Access to the MeSS database could be requested to the team. Currently, the infrastructure is not ready for a full public access, but the content is open for collaborators that want to explore Mercury.

Acknowledgments: The authors are very grateful to the SITU IT support team at the European Space Astronomy Centre (ESAC) for providing the required infrastructure.

[1] Besse, S. et al. (2015), JGR, Spectral properties of explosive volcanism within the Caloris basin with MESSENGER observations. 

[2] Besse, S. et al. (2020), JGR, Spectral properties and physical extent of pyroclastic deposits on Mercury: Variability within selected deposits and implications for explosive volcanism. 

[3] Barraud, O. et al. (2020), JGR, Near-Ultraviolet to Near-Infrared spectral properties of hollows on Mercury: Implications for origin and formation process.

[4] Rothery, D. et al. (2021), Icarus, On the asymmetry of Nathair Facula, Mercury.

[5] Barraud, O. et al. (2021), Icarus, Spectral investigation of Mercury’s pits surroundings: Constraints on the planet’s explosive activity.

How to cite: Besse, S., Munoz, C., and Cornet, T.: Revealing Mercury’s surface properties through data mining of the MESSENGER datasets with MeSS, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-102,, 2021.

Cristian Carli, Lorenza Giacomini, Francesca Zambon, Valentina Galluzzi, Sabrina Ferrari, Matteo Massironi, Francesca Altieri, Luigi Ferranti, Pasquale Palumbo, and Fabrizio Capaccioni

Introduction:  Kuiper quadrangle (H06) is located at the equatorial zone of Mercury and encompasses the area between longitudes 288°E – 360°E and latitudes 22.5°N – 22.5°S. A detailed geological map (1:3M scale) for this quadrangle based on the MESSENGER/MDIS derived maps, was produced [1] and an integrated geo-stratigraphic map is being prepared [2], [3].

The geological map showed that the quadrangle is characterized by a prevalence of crater materials, which were distinguished into three classes based on their degradation degree [4]. The different crater’s density permits to discriminate different plain units. Several bright regions are evidenced within H06, and in particular, different craters highlight both the presence of hollows and pyroclastic material [e.g., 5]. Some craters display very bright ejecta and Kuiper crater shows extended bright rays.

Data and Method: Multi-spectral maps of Kuiper quadrangle were produced using MDIS (Mercury Dual Imaging System) WAC data. Since H06 is located at low latitudes, the data coverage with high spatial resolution is lower than the northern quadrangles [e.g. 6-7]. This suggested  to approach the analysis in  multiple-stage passages. This has been done to investigate the H06 region with the best detail from a spectral point of view but also to compare the outcomes of the analysis performed at different resolution scales where it was possible. We produced an homogeneous 8 color global mosaic at 1600 m/px scale (average scale taking into account the average resolution within the quadrangle) and one at 665 m/pixel pushing the resolution, but excluding from the coverage the southern areas. Moreover, we used partial quadrangle coverage at 385 m/px and 246 m/px to exploit the presence of higher resolution color images.

Preliminary results: We will show spectral variations considering specific indices and color combinations, discussing the possibility to define spectral units among these bright regions, which could be integrated with the morpho-stratigraphic mapping. Locally, we will investigate the spectral variability for specific surface features, such as bright regions, fresh craters, hollows, pyroclastic deposits.

This analysis allows us to infer some indications on material composition and discriminate among the bright features, which seems to have an higher spectral variability (e.g. reflectance and slope variation) and an evidence of a possible absorption around 650 nm (e.g. Fig.1), already evidence in the literature [e.g. 8]. The definition of this compositional information permits also to produce a more detailed geological map of those regions in H06 showing correlations between morpho-stratigraphic and spectral units [2]. We also studied the variability with the spatial scale of spectral properties of different regions to investigate to the variation of this data varying the resolution on the surface. In Fig.2, for example, we show how the details change in the hollows field in the Warhol crater with the spectral differences from the same area for the two cases. Moreover the hollows spectral properties are characterizing an higher number of pixels where the spatial resolution is better. We will show some examples of such integrated maps.

Conclusions: In particular, at this preliminary stage, we can show that:

  • Bright regions have, in general, a reflectance variation ranging from 0.1 to 0.12 at 750 nm, but they show different spectral slopes and a larger reflectance variation towards the UV;
  • In general hollows and some small bright spot are bluer than pyroclastic materials and rays; furthermore, wherever hollows can be clearly identified by morphology they tend to show an indication of an absorption around 650 nm and an inflation towards IR;
  • When a higher spectral resolution can be used for the same R.O.I., we clearly see higher definition of spectral variability and higher number of pixel showing diagnostic features (e.g. hollow spectra).

This preliminary analysis highlights that both higher spectral and spatial resolutions are needed in order to obtain new information about the landforms’ origin. In light of these evidences, it appears that the high resolution of the instruments of BepiColombo mission, like SIMBIO-SYS, can significantly contribute to answer several questions raised during the geological mapping and analysis of the Kuiper quadrangle.

Acknowledgments: We gratefully acknowledge funding from the Italian Space Agency (ASI) under ASI-INAF agreement 2017-47-H.0. MM, CC, FZ, FA were also supported by European Union’s Horizon 2020 research grant agreement No 776276- PLANMAP. MM, CC, FA, GL, VG were also supported by Europlanet RI20-24 research grant agreement No. 871149-GMAP.

References: [1] Giacomini et al., 2018. EPSC abstract 721-1. [2] Giacomini et al., 2021. LPSC abstract, 15052. [3] Carli et al., 2020. EPSC abstract, 367. [4] Galluzzi et al., 2016. J. Maps, 12, 226–238. [5] Pajola et al., 2020. PSS, 195, . [6] Bott et al. 2019. JGR, 124, 2326-2346. [7] Zambon et al. 2019. EGU, 7796. [8] Lucchetti et al. 2018. JGR, 123, 2365–2379.

How to cite: Carli, C., Giacomini, L., Zambon, F., Galluzzi, V., Ferrari, S., Massironi, M., Altieri, F., Ferranti, L., Palumbo, P., and Capaccioni, F.: Spectral variability of brigth regions whitin Kuiper quadrangle: spectral indication for integrated geostratigraphic maps, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-238,, 2021.

Anna Galiano, Fabrizio Capaccioni, Gianrico Filacchione, and Cristian Carli


The explosive volcanism on Mercury [1,2,3,4] usually manifests with endogenic pits (vents) surrounded by bright deposits with a red spectral slope (faculae) [5,6]. The largest numbers of vents are on the floors, rims, central peaks or peak rings of impact structures, on faults, or within 20 km of a fault [7] and their presence suggests the explosive release of volatiles [8].

The Mercury’s pyroclastic deposits have been searched with Mercury Dual Imaging System (MDIS) data acquired during the first years of MESSENGER mission [9,10,6,11,12]. The sites of explosive volcanism have been identified by searching for faculae and then noting the occurrence of vents [9,10,6,11], obtaining a  catalogue of 137 candidate deposits (some of them of uncertain volcanic nature) [11]. Alternatively, MDIS images were first analyzed looking for pits and then for faculae [12]; this approach generated a catalogue of 174 candidate pits sites, of which 150 surrounded by faculae [12]. Analyses on the entire dataset of MESSENGER extended the catalogue to more than 200 vents [13,14].

In this work we are aiming to detect pyroclastic deposits by using unsupervised classification of the data acquired by Mercury Atmospheric and Surface Composition Spectrometer (MASCS) during the orbit phases of MESSENGER.



We analyzed MASCS photometrically-corrected spectral data acquired by the two channels of the Visible (300-1050 nm) and InfraRed (850-1450 nm) Spectrograph (VIRS). The two channels were merged at 989 nm and a Savitsky-Golay filter was applied on the spectra to reduce unwanted high-frequency artifacts present in the signal.

The spectral parameters used for the investigation focused on the VIS range and were: the spectral slope in the 300-400 nm range (UV slope); the spectral slope in the 500-800 nm range (VIS slope); the crossing point between the two slopes (knee); the reflectance value at 550 nm (R550). The slopes and the knee were estimated on normalized spectra at 550 nm to reduce remaining photometrical residuals.

The other important part of the work has been to appropriately select the spectra used for the classification. We thus limited the classified dataset to only those observations having optimal observing conditions: incidence and emission angles lower than 65°, footprint length lower than 25km and with a ratio of the footprint length over footprint width lower than 10. Furthermore, having studied the impact of the detector temperature on the SNR ratio we took into account only observations with VIS detector temperature lower than 25 °C.

To identify pyroclastic deposits we used 6 previously catalogued faculae as training fields. We mapped the values of R550 and spectral slopes in an area of 20°x20° around Rachmaninoff crater: in the analysis we included Suge Facula in the south-eastern part of crater, in addition to Neidr and Nathair Faculae, that are in the north of the area. Similarly, we mapped the spectral parameters in a region comprising the faculae in the floors of Catullus, Praxiteles and Lermontov craters (10-30°N; 40-80°W). As a result, the 6 faculae can be distinguished from the surrounding terrain for values of R550 included between 4% and 8% and with VIS slope values included between 1.7 and 2.0 1/µm.


Results and discussion

We applied the values of R550 and VIS slope detected by the analysis of the faculae used as training fields on the selected MASCS data and we obtained a map of locations spectrally similar to pyroclastic deposits, i.e. bright and with a red spectral slope. 37 locations correspond to pyroclastic deposits present in the published catalogues [6,9,10,11], 5 areas are coincident with uncertain deposits in [11], one is a red pitted ground in [12] and another area is a pit with no visible spectral anomaly in MDIS data [12]. In addition to the 44 previously identified locations, the unsupervised classification revealed more than 60 new areas spectrally similar to the faculae used as training fields.

In Figure 1, the Pentas crater is shown, with the classified MASCS pixels marked in red and coincident with the vents associated to the facula catalogued in [10]. The VIRS spectra associated to the pixels (Figure 2) are flat and featureless.


From a preliminary spectral analysis of some of the classified areas (19 catalogued faculae and 3 newly identified locations), we found a correlation between the R550 and the UV slope, whereas the knee and the VIS slope are anti-correlated. Taking into account all locations revealed by the unsupervised classification, the histogram of the UV slope (Figure 3, left image) assumes a bimodal trend, possibly suggesting some compositional differences in the underlying terrains: one with a low UV slope, probably connected to Low-Reflectance Blue Plain (LBP) and the other terrain with a higher UV slope, likely the High-Reflectance Red Plains (HRP). The histogram of the knee (Figure 3, right image) presents a main peak at 460 nm and a minor peak at 500 nm: recurrently, the spectra of the same area belong to both groups, suggesting a spectral variation within the same location.

Not all the deposits previously catalogued [6,9,10,11,12,13,14] did emerge by this unsupervised classification one reason being the lack of coverage. However, we shall improve the work enlarging the number of training fields to take into account the full range of the observed spectral variability of the faculae.



We gratefully acknowledge funding from the Italian Space Agency (ASI) under ASI-INAF agreement 2017-47-H.0. 



[1] Robinson,M.S. and Lucey, P.G., 1997, Science 275. [2] Strom, R.G. et al. , 1975, JGR 80. [3] Solomon S.C. et al., 2007, SSR 131. [4] Solomon S.C. et al., 2011, PSS 59. [5] Head. J.W. et al., 2008, Science 321. [6] Goudge, T.A. et al., 2014, JGR, Planets 119. [7] Klimczack, K.T. et al., 2018, Icarus 315. [8] Kerber, L. et al., 2009, EPSL. 285. [9] Blewett, D.T. et al., 2011, Science 333. [10] Kerber, L. et al., 2011, PSS 59. [11] Kerber, L. et al., 2014, 47th LPSC. [12] Thomas, T.J. et al., 2014, JGR Planets 119. [13] Jozwiak, L.M. et al., 2018, Icarus 302. [14] Pegg, D.L. et al., 2020, 2nd BPSC.

How to cite: Galiano, A., Capaccioni, F., Filacchione, G., and Carli, C.: Spectral identification of faculae on Mercury by unsupervised classification of MASCS/MESSENGER data, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-252,, 2021.

Giovanni Munaretto, Alice Lucchetti, Maurizio Pajola, Gabriele Cremonese, and Matteo Massironi

Introduction: The origin and formation of Hollows, puzzling surface features identified on MESSENGER (MErcury Surface, Space ENvironment, GEochemistry, and Ranging, [1]) images [2], represent one of the major open science questions about Mercury surface processes and landforms. Hollows are 10 m to several km-sized shallow, irregular, flat‐floored depressions characterized by bright interiors and haloes and found on crater walls, rims, floors, and central peaks [2,3]. Although a well-established framework explaining their nature and formation is still lacking, current hypotheses envision the release of volatiles from the surface of Mercury [1] through processes like sublimation, desorption, sputtering, micrometeorite impacts and pyroclastic volcanism. In this abstract, we analyze multi-angular Mercury Dual Imaging System (MDIS,[7]) wide angle camera (WAC) color images of hollows-hosting Canova (25.62°N, 3.75°W) and Tyagaraja craters (3.89 °N, 138.9°W), with the aim of investigating the spectrophotometric properties of these features.

Methodology:  We collected multiple MDIS/WAC images covering the hollows at Canova and Tjagaraya crater in 8 out of 11 filters ranging from 433 nm to 996 nm. Each image was processed with the ISIS software through the spiceinit and mdiscal and cam2map commands and downsampled to 665 m/px. The phocube task was used to compute the local incidence, emission, phase and solar and spacecraft azimuth. Next, a sampling grid with a scale of 665 m was constructed (Fig. 1A). For all observations in each filter, the flux and the photometric angles are collected at each sampling point (Fig. 1A, 1B). This data was fitted with a basic Hapke model and different version of the Kasalaainen-Shkuratov models (KS1 to KS6 as defined in [8]). The fitted parameters were used to correct each observation to the standard illumination and observation geometry (i.e., incidence=30° and emission=0°) and average all the images in each band to obtain spectra at a higher SNR than those based on a single image of the same resolution.

Figure 1 A) Sketch of the MDIS/WAC Canova dataset. B) WAC G-band image coverage (violet, in transparency. C) Area under investigation (pink square). D)  Boxplots of the ratio between the observed and predicted flux for each fitted model.

Results and discussion: The performance of our correction is shown by the box-plots in Fig. 1D. On the y-axis we report the ratio of the observed vs predicted flux for the I (996.2 nm) band. We plot only 1 band for clarity, but the others give similar results. The fitted photometric models allow to reproduce the observed flux values with an error below 10% at 3σ, which is comparable with the radiometric accuracy of the MDIS/WAC datasets.

Figure 2 A) MDIS/NAC global mosaic of Mercury showing Canova crater. The blue and brown areas represent the Canova hollows (CA HOL). And crater floor material (CA CF) ROIs, respectively. B) MDIS/WAC image of Tyagaraja crater. Lightblue and gold areas are the Tyagaraja hollow (TY HOL) and crater floor material (TY CF) ROIs, respectively.


Figure 3 Continuum removed reflectance spectra of Canova and Tyagaraya hollows (CA HOL, TY HOL) and crater floor material (CA CF, TY CF).

Continuum removed reflectance spectra of the hollows and crater floor materials (“CA HOL” and “CA CF” ROIs in Fig. 2A for Canova ,“TY HOL” and TY CF” ROIs in Fig. 2B for Tyagaraja) were extracted from the average of all the corrected images in each band and are shown in Fig. 3. Consistently with the multi-band, MDIS/WAC, clustering-derived spectra from [5], our hollows spectra show an absorption between 600 and 800 nm. From the same ROIs we also extracted the best-fit parameters for all the photometric models. The distribution of the Hapke single-scattering albedo for the hollows and the crater floor material is shown in Fig. 3. In particular, hollows have a higher single-scattering albedo and a more spread distribution than the crater floor material. Interestingly, the hollows distributions are very similar for both craters.