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Enregistrement W4206006039 · doi:10.5194/epsc2021-379

Multiband photometry of Martian Recurring Slope Lineae (RSL) and dust-removed features at Horowitz crater, Mars from TGO/CaSSIS color observations

2021· preprint· en· W4206006039 sur OpenAlexaff
Giovanni Munaretto, M. Pajola, Alice Lucchetti, G. Cremonese, Emanuele Simioni, Cristina Re, Silvia Bertoli, L. L. Tornabene, A. S. McEwen, P. Becerra, Vidhya Ganesh Rangarajan, Adomas Valantinas, A. Pommerol, N. Thomas, Ganna Portyankina

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomainePhysics and Astronomy
ThématiquePlanetary Science and Exploration
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésMartianImpact craterMars Exploration ProgramPhotometry (optics)Martian surfaceGeologyAstrobiologyAeolian processesMineralogyAstrophysicsPhysicsGeomorphology

Résumé

récupéré en direct d'OpenAlex

Introduction: Recurring Slope Lineae (RSL) are narrow dark streaks that incrementally lengthen down Martian warm steep slopes [1]. On first approximation, they appear during the Martian summer, disappear in winter and recur annually. This temperature dependence suggested that RSL may be related to flows of liquid water or brines [1-5], possibly fed by groundwater sources [6, 7] or from the deliquescence of hygroscopic salts [8]. Alternatively, RSL have been interpreted as dry granular flows [9, 10, 11, 12], possibly related to aeolian processes [13, 14]. Although a detailed explanation of the RSL nature and formation mechanism is still not complete, the latest evidence points further toward a dry mechanism [15].Here, we provide further evidence for a dry origin of RSL by investigating their colour properties through multiband photometry obtained from 4-filter Colour and Surface Science Imaging System (CaSSIS, [16]) observations at Horowitz crater, Mars. We compare it with multiband photometry of dark tracks left by the passage dust-devils (DDTs), which are dark marks left by the passage of whirlwinds, and regions where surface dust has been partially removed (“dust poor” regions, DPs). Since DDTs and DPs are being formed by ferric dust removal and the exposure of underlying, typically ferrous material, the comparative multiband photometry provided by CaSSIS may help us understand whether RSL are consistent with being formed by the removal of dust. This would contribute to show that RSLs are indeed dry flows of dust and sand. Our comparative photometry is then supported by a photometric modeling approach.Figure 1 CaSSIS NPB (i.e., NIR PAN, BLU) colour composite showing the central peak of Horowitz crater and the location of the RSL, DDTs and “dust-poor” (DP) ROIs. “DR” indicate dust rich regions, “DE” indicate “dust-enriched” regions (i.e., with an intermediate dust content between DP and DR) Methodology: We adapted the technique of [11,12] to compute the relative reflectance of RSL in the NIR (936.7 nm), RED (836.2 nm), PAN (675.0 nm) and BLU (499.9 nm) BLU CaSSIS filters. To do this, we select several regions of interest (ROIs) within RSLs, DDTs and DPs at Horowitz crater. The considered ROIs are shown in Fig. 1. We compute for each filter the RSL relative reflectance as in [11,12] and we repeat the procedure for DDTs and DPs. In our calculations, we apply a first order atmospheric correction by subtracting the I/F of the darkest pixel of each filter before computing the relative reflectance [16,17,18]. The relative albedo profiles are then compared with photometric models of dust deposition derived from the laboratory experiments of [19] and with mixtures of wet and dry martian terrain simulants derived from [20]. Figure 2. Relative albedo of A) RSL B) DDTs and C) DP ROIs with respect to nearby materials and D) corresponding average profileFigure 3. A) RSL and best-fit dust models. The legend reports the estimated dust content for the RSL (numerator) and corresponding nearby material (denominator). B) RSL and best-fit water models. The legend reports the best-fit water contentResults: RSL, DDTs and DPs have similar relative reflectance profiles (Figure 2) within errors, i.e., they are all brighter in the BLU than in the PAN, RED and NIR filters. While there is some variability, there are no significant spectral features that characterize either RSL or DDTs or DPs in the latter three bands, where all show an approximately constant relative reflectance. In addition, photometric models of dust fallout provide significantly better fits than mixtures of dry and wet martian terrain simulants.Discussion and conclusions: The comparison between relative photometry of RSLs, DDTs and DPs in the 4 CaSSIS filters show that these features have a quite similar relative reflectance profile, suggesting that they may have a similar origin. In particular, the higher BLU with respect to PAN, RED and NIR relative reflectance may suggest that the reflectance profiles of both features could be consistent with a ratio between a ferrous material (numerator), exposed by the removal of a ferric surface material such as the Martian dust (denominator). This picture is supported by comparison with photometric model of dust fallout, that consistently provide better fits to the observed relative reflectances than mixtures of dry and wet martian soils.Acknowledgments: The authors wish to thank the spacecraft and instrument engineering teams for the successful completion of the instrument. CaSSIS is a project of the University of Bern and funded through the Swiss Space Office via ESA’s PRODEX programme. The instrument hardware development was also supported by the Italian Space Agency (ASI) (ASI – INAF agreement no. I/018/12/0), INAF/Astronomical Observatory of Padova, and the Space Research Center (CBK) in Warsaw. Support from SGF (Budapest), the University of Arizona (Lunar and Planetary Lab.) and NASA are also gratefully acknowledged. The study has been supported by the Italian Space Agency (ASI-INAF agreement no. 2020-17-HH.0). References: [1] A. S. McEwen et al. (2011), Science (6043), 740–743 [2] Levy, J et al. (2012), Icarus, 219, (1), 1-4 [3] McEwen et al. (2014), Nat. Geosci. 7 (1), 53–58 [4] Stillman et al. (2014), Icarus 233, 328–341 [5] Huber et al., (2020) Icarus 335, 113385. [6] Stilllman et al. (2016), Icarus 265, 125–138. [7] Abotalib and Heggy (2019) ,Nat. Geosci. 12, 235–241 [8] Wang et al. (2020), Icarus 333, 464–480 [9] Dundas et al. (2017), Nat. Geosci. 10 (12), 903–907 [10] Schmidt et al. (2017), Nat. Geosci. 10 (4), 270–273 [11] Schaefer et al. (2019), Icarus 317, 621–648 [12] Munaretto et al. (2020), PSS 187, (2020), 104947 [13] Vincendon et al. (2019), Icarus 325, 115–127 [14] Dundas et al. (2020), Icarus 343, 2020, 113681 [15] McEwen, A. S. et al. (2021), JGR-Planets, in press. [16] Thomas et al. (2017) Space Sci. Rev. 212 (3–4), 1897–1944[17] Chavez Jr, P. S. (1988) .Remote sensing of environment, 24(3), 459-479. [18] Tornabene et al. 2018, SSR,214(1),18 [19] Wells et al., 1984, Icarus 58, 3, 331-338 [20] Gunderson et al 2007, PSS, 55, 10, 1272-1282

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,246
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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