Laser altimetry experiments on the NASA MESSENGER mission [1], and on the currently on cruise ESA/JAXA BepiColombo Mission [2,3] did and are going to yield, respectively, a plethora of range measurements of the surface of Mercury. Orbital laser altimetry can be used to derive tidal parameters, which can in turn be used to infer properties of a body’s interior [4,5]. The derivation of tidal parameters requires large datasets of precise and accurate measurements. Errors as well as outliers can degrade the quality of the computed tidal parameters. While many outliers can be filtered though conventional automated processes, other errors could only be identified by human supervision. In the face of the amount of data involved, systematic user interaction at the error identification step becomes unpractical. A neural network trained with user expertise could help spotting outliers and errors and would improve the derived parameters in accuracy and precession. We started developing a neural network based on the pytorch framework[6] and compared the performance with a small training dataset form the MESSENGER Laser Altimeter (MLA) for a linear and a convolutional network. The results were much in favour of the linear network [7]. In this presentation we explore the reasons behind bad convolutional network performance with extended training and test datasets. We are going to show our results for the filtered datasets and the impact this has on the derived tidal parameters. The filtering with an artificial neural network might be useful for other applications, as well. 1. Cavanaugh, J. F. et al. The Mercury Laser Altimeter Instrument for the MESSENGER Mission. Space Sci Rev 131, 451–479 (2007). 2. Benkhoff, J. et al. BepiColombo—Comprehensive exploration of Mercury: Mission overview and science goals. Planetary and Space Science 58, 2–20 (2010). 3. Thomas, N. et al. The BepiColombo Laser Altimeter. Space Sci Rev 217, 25 (2021). 4. Thor, R. N. et al. Determination of the lunar body tide from global laser altimetry data. J Geod 95, 4 (2021). 5. Thor, R. N. et al. Prospects for measuring Mercury’s tidal Love number h2 with the BepiColombo Laser Altimeter. A&A 633, A85 (2020). 6. Paszke, A., et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, In: Advances in Neural Information Processing Systems 32, pp 8024–8035, 2019. 7. Stenzel, O., Thor, R., and Hilchenbach, M.: Error identification in orbital laser altimeter data by machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14749, https://doi.org/10.5194/egusphere-egu21-14749, 2021.

We analyze data of the Mercury Laser Altimeter (MLA) aboard the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft to infer Mercury’s reaction to tidal forces. The combination of the surface displacement, parametrized by the Love number h2, and the change in the gravity field, parametrized by the Love number k2, due to tides, can be used to reveal the existence and size of Mercury’s solid inner core (Steinbrügge et al., 2018). This in turn provides important constraints on Mercury’s thermal evolution and dynamo. The MLA has delivered more than 40 million measurements of the shape of Mercury at about 26 million individual footprint locations. These locations are focused around the North Pole, with 78.5% of all measurements located between 50° N and 84° N. The topography at the footprint location is determined from the position of the spacecraft, the orientation of the instrument, and the measured range to the surface. Previously, two methods have been proposed for the retrieval of tidal surface displacements from laser altimetry data: The crossover method (e.g., Mazarico et al., 2014) and a global approach, which simultaneously solves for global topography and h2 (Thor et al., 2020). In the global approach, the topography is parametrized as an expansion in 2D cubic B-splines on an equirectangular grid with resolutions reaching up to 32 grid cells per degree. Here, we investigate the possibility to substitute the solution for topography in the global approach by the usage of a prescribed topographic model. This so-called direct altimetry approach has not been applied to the problem of retrieving tides from laser altimetry yet. We use a high-resolution (64 pix / degree) digital terrain model (DTM) obtained by photogrammetric evaluation of images obtained by the Mercury Dual Imaging System (MDIS, Becker et al., 2016) and compare the MLA topographic values to the corresponding values at the MLA measurement locations interpolated from the MDIS DTM.

The detailed and accurate shape of the terrain is necessary for some fields, such as geodetic analysis and geomorphology, which are concerned with the physical processes of a planet and its orbital attitudes. The Mercury Laser Altimeter (MLA) onboard the MESSENGER orbiter has performed successive altimetry measurements between 2011 and 2015. These measurements provide a global shape of the planet with approximately 3200 laser profiles, with the majority of these profiles covering the northern hemisphere due to MESSENGER's highly elliptical orbit (Cavanaugh et al., 2007). A number of studies have sought to derive digital terrain models (DTMs) of Mercury using stereo-imaging techniques (Fassett, 2016; Manheim et al., 2022, Florinsky, 2018). Some have employed MLA data (Zuber et al., 2012; Preusker et al., 2017). However, challenges remain due to discrepancies in the laser altimeter tracks, which are thought to be due to residual errors in spacecraft orbit, pointing, etc. Filtering these offsets provides an opportunity for further refinement and improvement of DTM generation methods for Mercury's surface.The aim of this paper is to accurately extract terrain from MLA point clouds, particularly around the characteristic features of the northern hemisphere, at high resolutions. In this work, we propose an algorithm that follows a region-growing approach to extract the terrain. To identify the optimal surface solution in each local patch (Arghavanian, 2022), we apply machine learning methods and statistical parameters, starting from regions that are relatively easy to fit and then growing to the most challenging areas. Additionally, users have the flexibility to add or remove constraints or fine-tune terrain-relevant and empirical parameters based on their specific topography. The final surface is obtained by global surface fitting, thereby filling any gaps in the data. To evaluate the performance of the proposed method, the results will be compared with previously produced DEMs.References: Arghavanian A., 2022, Channel detection and tracking from LiDAR data in complicated terrain, PhD thesis, Middle East Technical University, Ankara, Turkey. Cavanaugh, J.F. et al. (2007) ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’, Space Science Reviews, 131(1), pp. 451–479. Available at: https://doi.org/10.1007/s11214-007-9273-4.Fassett C.I., 2016, Ames stereo pipeline-derived digital terrain models of Mercury from MESSENGER stereo imaging, Planetary and Space Science, Volume 134, Pages 19-28.Florinsky I.V., 2018, Multiscale geomorphometric modeling of Mercury, Planetary and Space Science Volume 151, Pages 56-70.Manheim M.R., Henriksen M.R., Robinson M.S., Kerner H. R., Karas B.A., Becker K.J., Chojnacki M., Sutton S.S., Blewett D.T., 2022, High-Resolution Regional Digital Elevation Models and Derived Products from MESSENGER MDIS Images, Remote Sens, 14, 3564.Preusker F, Stark A, Oberst J, Matz K.D., Gwinner K, Roatsch T, Watters T.R., 2017, Planetary and Space Science, Volume 142, Pages 26-37.Zuber, M.T. et al. (2012) ‘Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry’, Science, 336(6078), pp. 217–220. Available at: https://doi.org/10.1126/science.1218805.

During ESA's Rosetta science mission, the COSIMA instrument collected dust particles in the coma of Comet 67P/Churyumov-Gerasimenko during two years near the comet's nucleus. The largest particles are about 1mm in size. The collection process involved a low velocity impact on porous gold-black surfaces, often resulting in breakup, from which information on structural properties has previously been derived (Langevin et al., 2016). However, some of the particles were collected with little damage, but fragmented due to charging during subsequent secondary ion mass spectrometry. This report shows that the details of this electrical fragmentation support the concept of the existence of stable units with sizes of tens of μm within the incoming cometary dust particles prior to collection, possibly representing remnants of the early accretion processes.

Laser altimeters create large amounts of data that often have to be preprocessed and checked before further use. The BepiColombo mission to Mercury is set to arrive in December 2025 and observations with the BepiColombo Laser Altimeter (BELA, (Benkhoff et al., 2010; Thomas et al., 2021)) will start during the following spring. These measurements are planned to be used to derive information about the tides of Mercury (Thor et al., 2020). Careful assessment, selection, and filtering on the raw data is needed to extract the small tidal signal. Until the BELA data becomes available artificial data and records from other missions have to be used to study the data selection strategy. We present our work on MESSENGER Laser Altimeter (MLA, (Cavanaugh et al., 2007)) using a convolutional neural network to sort observations on an orbit by orbit basis into different classes. The already existing neural network (Stenzel and Hilchenbach, 2021; Stenzel, Thor and Hilchenbach, 2021) is tuned and a new test data set is created. Benkhoff, J. et al. (2010) ‘BepiColombo—Comprehensive exploration of Mercury: Mission overview and science goals’, Planetary and Space Science, 58(1), pp. 2–20. Available at: https://doi.org/10.1016/j.pss.2009.09.020. Cavanaugh, J.F. et al. (2007) ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’, Space Science Reviews, 131(1), pp. 451–479. Available at: https://doi.org/10.1007/s11214-007-9273-4. Stenzel, O. and Hilchenbach, M. (2021) ‘Towards machine learning assisted error identification in orbital laser altimetry for tides derivation’, pp. EPSC2021-688. Available at: https://doi.org/10.5194/espc2021-688. Stenzel, O., Thor, R. and Hilchenbach, M. (2021) ‘Error identification in orbital laser altimeter data by machine learning’, pp. EGU21-14749. Available at: https://doi.org/10.5194/egusphere-egu21-14749. Thomas, N. et al. (2021) ‘The BepiColombo Laser Altimeter’, Space Science Reviews, 217(1), p. 25. Available at: https://doi.org/10.1007/s11214-021-00794-y. Thor, R.N. et al. (2020) ‘Prospects for measuring Mercury’s tidal Love number h2 with the BepiColombo Laser Altimeter’, Astronomy & Astrophysics, 633, p. A85. Available at: https://doi.org/10.1051/0004-6361/201936517.

<p>During the ESA ROSETTA science mission to comet 67P/Churyumov-Gerasimenko, the dust particle analysing instrument COSIMA sampled dust in the size range from a few um to mm equivalent diameter in the inner coma of the comet.  The particles were analysed in-situ with an optical microscope and a secondary ion mass spectrometer. The dust particles were collected on porous gold black surfaces with relative low impact velocity and the break up or fragmentation due to impact as well as by mechanical and/or electrical means have been studied. We summarize the results and conclusions on the measured mechanical, optical and electrical parameters such as porosity, material strength, reflectance, electrical conductivity and relative permittivity of the dust particles.</p>

Introduction: The Rosetta mission is one of the latest great scientific and technological European successes. The probe and its lander Philae, transported some very audacious and inventive instruments that for some worked beyond expectations, and gave the scientists and engineers involved an expertise and experience that we should try to build on. The COmetary Secondary Ion Mass Analyser (COSIMA) onboard Rosetta, was the first instrument applying in situ analyses of cometary grains [1]. This instrument already combined two techniques: visible microscopy that was crucial to detect routinely the collected dust and characterize its structure [2], and Time-Of-Flight Secondary Ion Mass Spectrometry that mainly allowed us to determine the elemental composition of the dust [3-4]. COSIMA covered a mass range 1–1200 u but was limited to a mass resolution m/Δm of 1400 at mass 100 u at Full Width Half Maximum (FWHM), which made the assignment of molecular signals difficult. For the next generation of extraterrestrial (especially, primitive) dust analyzers, we are proposing an instrument that combines near infrared (NIR) and visible microscopy with laser ionization mass spectrometry (LIMS). This multi-analysis instrument (named dPCA for dust Particle Composition Analyzer) was part of the Castalia+ mission proposition to the ESA M7 call, but it is potentially suited for any space mission aiming to characterize dusty materials, especially complex ones containing organic and mineral phases. In the prospect to build a new generation of mass spectrometer offering High Resolution Mass Spectrometry (HRMS) collaborative effort between consortium of French and Czech laboratories (LPC2E, LATMOS, LISA, IPAG, IJCLab, J. Heyrovsky Institute of Physical Chemistry) and University of Maryland and NASA GSFC, are on-going to settle a pulsed UV laser source with an Orbitrap™ [5] mass analyzer for planetary applications. The spaceflight and ruggedized version of the Orbitrap cell and its electronics (preamplifier, ultra-stable High Voltage), the CosmOrbitrap mass analyzer/detector, is capable of discriminating isobaric interferences with ultrahigh mass resolution m/Δm > 100,000 (FWHM), high mass accuracies and dual polarity measurements [6-10]. The NIR channel of the microscope will be based on the MicrOmega hyperspectral instrument developed at IAS (Orsay, France). Several replicas of this instrument have already flown or are currently working aboard MASCOT/Hayabusa2, on the ExoMars rover [11] and currently in the JAXA curation facility for Ryugu dust analysis [12]). In this study, we investigated the analytical value of combining these two techniques. We will show through laboratory measurements how combining infrared and mass data could be extremely useful to unambiguously characterize the targeted dust. Experimental Procedure: The main well known inputs given by the NIR spectra are: the detection and characterization of hydration signatures, especially on silicates, the detection and partial characterization of carbonates and sulfates, the detection of ammoniated compounds, the detection of the presence of organic compounds and the detection and identification of ices (H2O, CO2, etc.). In the context of main belt comets, asteroids and even the Martian moons and surface, the full characterization of hydration signatures is one of the most important information needed to complete the mass spectra characterization. For this reason, we started our set of experiments and measurements on both instruments, a laboratory MicrOmega replica at IAS, and a laboratory instrument prototype of a laser ablation / ionization Orbitrap™ mass spectrometer, the LAb-CosmOrbitrap, developed at LPC2E (Orléans, France), by focusing on hydrated and anhydrous silicates. The LAb-CosmOrbitrap integrates i) a commercial pulsed Nd-YAG laser used at 266 nm UV wavelength, ii) a set of ion focusing lenses without C-trap, and iii) a spaceflight CosmOrbitrap mass analyzer/detector. Variable output energy of laser beam can be operated thanks to a polarizing prism. Samples: To explore the capabilities of the LAb-CosmOrbitrap instrument to characterize silicate in both positive and negative ion mode, we started with analyses of a San Carlos olivine, and a natural Mg-rich serpentine dominated by antigorite and chrysotile. Both samples are obviously silicates, but the former is an anhydrous ionic solid (no covalent bond between O and Mg) and the latter is a phyllosilicate (= a hydrated silicate) where Mg is covalently bonded to O and OH. Same samples have been analyzed with MicrOmega replica instrument. Results: LAb-CosmOrbitrap measurements in positive ion modes of the serpentine sample enables detection of various oxide and hydroxide magnesium peaks at high mass accuracy (< 2.5 ppm) during a single laser shot experiment, with high mass resolution (for examples m/∆m ~ 160,000 (FWHM) for 24MgOH+ and m/∆m > 130,000 (FWHM) for 24Mg2O+). In this study, we explored the best suitable laser energy and consecutive shot sequences to find reproducible and robust measurements of oxide and hydroxide ions. These results will be presented among those on olivine and the spectra obtained with NIR microscopy. Conclusions: This study demonstrates the capabilities of a UV Laser-CosmOrbitrap instrument combined with a NIR spectrometer to detect and characterize hydrated signatures of a serpentine with an optimized protocol. Next steps that will be pursued are among others the analyses of other types of silicates and minerals, of silicate doped with organic compounds. In the prospect of a future space instrument, it is obvious that IR microscopy would be of great benefit for a fast screening of the area of the targeted dust in order to further perform LIMS analyses and increase the confidence in the identification of molecular and structural indices. Acknowledgement: We thank the Centre National des Etudes Spatiales (CNES) for their financial support. References: [1] Hilchenbach et al. (2016) ApJL 816, L32. [2] Langevin et al. (2016) Icarus 271, 76–97. [3] Fray et al. (2016) Nature 538, 72–74. [4] Bardyn, Baklouti et al. (2017) MNRAS 469, S712–S722. [5] Makarov (1999) US Patent 5, 886, 346. [6] Briois et al. (2016) PSS 131, 33–45. [7] Arevalo Jr. et al. (2018) Rapid Comm 32, 1875–1886. [8] Selliez et al. (2019) PSS 170, 42–51. [9] Selliez et al. (2020) Rapid Comm 34, e8645. [10] Cherville et al. (2021) COSPAR2021, Abstract B0.4-0018-21. [11] Pilorget and Bibring (2014) PSS 99, 7–18. [12] Pilorget et al. (2021) Nature Astronomy 6, 221–225.

The ESA/Jaxa mission BepiColombo[1] to Mercury will arrive in orbit in 2025. Onboard is the BepiColombo Laser Altimeter (BELA)[2], which will be used to scan the planetary surface. We plan on using these data to derive the tidal parameter h2[3]. To achieve an accurate result it is necessary to identify and eliminate noise in the laser altimeter records[4]. We present a strategy to find[5], [6] and neutralize the non-Gaussian noise contributions which contribute the most to the uncertainty in the derived Love number. References [1] J. Benkhoff et al., ‘BepiColombo—Comprehensive exploration of Mercury: Mission overview and science goals’, Planet. Space Sci., vol. 58, no. 1, pp. 2–20, Jan. 2010, doi: 10.1016/j.pss.2009.09.020. [2] N. Thomas et al., ‘The BepiColombo Laser Altimeter’, Space Sci. Rev., vol. 217, no. 1, p. 25, Feb. 2021, doi: 10.1007/s11214-021-00794-y. [3] R. N. Thor et al., ‘Prospects for measuring Mercury’s tidal Love number h2 with the BepiColombo Laser Altimeter’, Astron. Astrophys., vol. 633, p. A85, Jan. 2020, doi: 10.1051/0004-6361/201936517. [4] O. J. Stenzel, I. Hall, and M. Hilchenbach, ‘Mercury Tide Parameter Estimation from Laser Altimeter Records’, LPI contributions vol. 2678, p. 1990, Mar. 2022. [5] O. Stenzel and M. Hilchenbach, ‘Towards machine learning assisted error identification in orbital laser altimetry for tides derivation’, pp. EPSC2021-688, Sep. 2021, doi: 10.5194/espc2021-688. [6] O. Stenzel, R. Thor, and M. Hilchenbach, ‘Error identification in orbital laser altimeter data by machine learning’, pp. EGU21-14749, Apr. 2021, doi: 10.5194/egusphere-egu21-14749.

Orbital Laser altimeters deliver a plethora of data that is used to map planetary surfaces [1] and to understand interiors of solar system bodies [2]. Accuracy and precision of laser altimetry measurements depend on the knowledge of spacecraft position and pointing and on the instrument. Both are important for the retrieval of tidal parameters. In order to assess the quality of the altimeter retrievals, we are training and implementing an artificial neural network (ANN) to identify and exclude scans from analysis which yield erroneous data. The implementation is based on the PyTorch framework [3]. We are presenting our results for the MESSENGER Mercury Laser Altimeter (MLA) data set [4], but also in view of future analysis of the BepiColombo Laser Altimeter (BELA) data, which will arrive in orbit around Mercury in 2025 on board the Mercury Planetary Orbiter [5,6]. We further explore conventional methods of error identification and compare these with the machine learning results. Short periods of large residuals or large variation of residuals are identified and used to detect erroneous measurements. Furthermore, long-period systematics, such as those caused by slow variations in instrument pointing, can be modelled by including additional parameters. [1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. ‘Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry’. Science 336, no. 6078 (13 April 2012): 217–20. https://doi.org/10.1126/science.1218805. [2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gläser, Alexander Stark, Gregor Steinbrügge, and Jürgen Oberst. ‘Determination of the Lunar Body Tide from Global Laser Altimetry Data’. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8. [3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. ‘PyTorch: An Imperative Style, High-Performance Deep Learning Library’. Advances in Neural Information Processing Systems 32 (2019): 8026–37. [4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’. Space Science Reviews 131, no. 1 (1 August 2007): 451–79. https://doi.org/10.1007/s11214-007-9273-4. [5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. ‘The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design’. Planetary and Space Science 55, no. 10 (1 July 2007): 1398–1413. https://doi.org/10.1016/j.pss.2007.03.003. [6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. ‘BepiColombo—Comprehensive Exploration of Mercury: Mission Overview and Science Goals’. Planetary and Space Science, Comprehensive Science Investigations of Mercury: The scientific goals of the joint ESA/JAXA mission BepiColombo, 58, no. 1 (1 January 2010): 2–20. https://doi.org/10.1016/j.pss.2009.09.020.

Between Aug. 2014 and Sept. 2016, while ESA's cornerstone mission Rosetta was operating in the vicinity of the nucleus and in the coma of comet 67P/Churyumov-Gerasimenko, the COSIMA instrument collected a large number of dust particles with diameters up to a millimeter. Positive or negative ions were detected by a time-of-flight secondary ion mass spectrometer (TOF-SIMS) and the composition of selected particles was deduced. Many of the negative ion mass spectra show, besides mass peaks at the correct position, an additional, extended contribution at the lower mass side caused by partial charging of the dust. This effect, usually avoided in SIMS applications, can in our case be used to obtain information on the electrical properties of the collected cometary dust particles, such as the specific resistivity (ρr>1.2⋅1010Ωm) and the real part of the relative electrical permittivity (εr<1.2). From these values a lower limit for the porosity is derived (P>0.8).

The Cometary Secondary Ion Mass Analyzer (COSIMA) onboard ESA's Rosetta orbiter has revealed that dust particles in the coma of comet 67P/Churyumov-Gerasimenko are aggregates of small grains. We study the morphological, elastic, and electric properties of dust aggregates in the coma of comet 67P/Churyumov-Gerasimenko using optical microscopic images taken by the COSIMA instrument. Dust aggregates in COSIMA images are well represented as fractals in harmony with morphological data from MIDAS (Micro-Imaging Dust Analysis System) and GIADA (Grain Impact Analyzer and Dust Accumulator) onboard Rosetta. COSIMA's images, together with the data from the other Rosetta's instruments such as MIDAS and GIADA do not contradict the so-called rainout growth of 10 ​μm-sized particles in the solar nebula. The elastic and electric properties of dust aggregates measured by COSIMA suggest that the surface chemistry of cometary dust is well represented as carbonaceous matter rather than silicates or ices, consistent with the mass spectra, and that organic matter is to some extent carbonized by solar radiation, as inferred from optical and infrared observations of various comets. Electrostatic lofting of cometary dust by intense electric fields at the terminator of its parent comet is unlikely, unless the surface chemistry of the dust changes from a dielectric to a conductor. Our findings are not in conflict with our current understanding of comet formation and evolution, which begin with the accumulation of condensates in the solar nebula and follow with the formation of a dust mantle in the inner solar system.

The instrument COSIMA (COmetary Secondary Ion Mass Analyzer) onboard of the European Space Agency mission Rosetta collected and analyzed dust particles in the neighborhood of comet 67P/Churyumov-Gerasimenko. The chemical composition of the particle surfaces was characterized by time-of-flight secondary ion mass spectrometry. A set of 2213 spectra has been selected, and relative abundances for CH-containing positive ions as well as positive elemental ions define a set of multivariate data with nine variables. Evaluation by complementary chemometric techniques shows different compositions of sample groups collected during two periods of the mission. The first period was August to November 2014 (far from the Sun); the second period was January 2015 to February 2016 (nearer to the Sun). The applied data evaluation methods consider the compositional nature of the mass spectral data and comprise robust principal component analysis as well as classification with discriminant partial least squares regression, k-nearest neighbor search, and random forest decision trees. The results indicate a high importance of the relative abundances of the secondary ions C+ and Fe+ for the group separation and demonstrate an enhanced content of carbon-containing substances in samples collected in the period with smaller distances to the Sun.

The instrument Cometary Secondary Ion Mass Analyzer (COSIMA) on board of the European Space Agency mission Rosetta to the comet 67P/Churyumov‐Gerasimenko is a secondary ion mass spectrometer with a time‐of‐flight mass analyzer. It collected near the comet several thousand particles, imaged them, and analyzed the elemental and chemical compositions of their surfaces. In this study, variables have been generated from the spectral data covering the mass ranges of potential C‐, H‐, N‐, and O‐containing ions. The variable importance in binary discriminations between spectra measured on cometary particles and those measured on the target background has been estimated by the univariate t test and the multivariate methods discriminant partial least squares, random forest, and a robust method based on the log ratios of all variable pairs. The results confirm the presence of organic substances in cometary matter—probably a complex macromolecular mixture.