Abstract. A highly miniaturized limb sounder for the observation of the O2 A-band to derive temperatures in the mesosphere and lower thermosphere is presented. The instrument consists of a monolithic spatial heterodyne spectrometer (SHS), which is able to resolve the rotational structure of the R-branch of that band. The relative intensities of the emission lines follow a Boltzmann distribution and the ratio of the lines can be used to derive the kinetic temperature. The SHS operates at a Littrow wavelength of 761.8 nm and heterodynes a wavelength regime between 761.9 and 765.3 nm with a resolving power of about 8000 considering apodization effects. The size of the SHS is 38 × 38 × 27 mm3 and its acceptance angle is ±5∘. It has an etendue of 0.01 cm2 sr. Complemented by front optics with an acceptance angle of ±0.65∘ and detector optics, the entire optical system fits into a volume of about 1.5 L. This allows us to fly this instrument on a 3- or 6-unit CubeSat. The vertical field of view of the instrument is about 60 km at the Earth's limb when operated in a typical low Earth orbit. Integration times to obtain an entire altitude profile of nighttime temperatures are on the order of 1 min for a vertical resolution of 1.5 km and a random noise level of about 1.5 K. Daytime integration times are 1 order of magnitude shorter. This work presents the design parameters of the optics and a radiometric assessment of the instrument. Furthermore, it gives an overview of the required characterization and calibration steps. This includes the characterization of image distortions in the different parts of the optics, visibility, and phase determination as well as flat fielding.
The evolution of the solar system and the origin of life remain some of the most intriguing questions for humankind. Addressing these questions experimentally is challenging due to the difficulty of mimicking environmental conditions representative for Early Earth and/or space conditions in general in ground-based laboratories. Performing experiments directly in space offers the great chance to overcome some of these obstacles and to possibly find answers to these questions. Exposure platforms in Low Earth Orbit (LEO) with the possibility for long-duration solar exposure are ideal for investigating the effects of solar and cosmic radiation on various biological and non-biological samples. Up to now, the Exobiology and space science research community has successfully made use of the International Space Station (ISS) via the EXPOSE facility to expose samples to the space environment with subsequent analyses after return to Earth. The emerging small and nanosatellite market represents another opportunity for astrobiology research as proven by the robotic O/OREOS mission, where samples were monitored in-situ, i.e. in Earth orbit. In this framework, the European Space Agency is developing a novel Exobiology facility outside the ISS. The new platform, which can host up to four different experiments, will combine the advantages of the ISS (long-term exposure, sample return capability) with near-real-time in-situ monitoring of the chemical/biological evolution in space. In particular, ultraviolet-visible (UV-Vis) and infrared (IR) spectroscopy were considered as key non-invasive methods to analyse the samples in situ. Changes in the absorption spectra of the samples developing over time will reveal the chemical consequences of exposure to solar radiation. Simultaneously, spectroscopy provides information on the growth rate or metabolic activities of biological cultures. The first quartet of experiments to be performed on-board consists of IceCold, OREOcube and Exocube (dual payload consisting of ExocubeChem and ExocubeBio). To prepare for the development of the Exobiology facility, ground units of the UV-Vis and IR spectrometers were studied, manufactured and tested as precursors of the flight units. The activity led to a modular in-situ spectroscopy platform able to perform different measurements (e.g. absorbance, optical density, fluorescence measurements) at the same time on different samples. We describe here the main features of the ground model platform, the verification steps, results and approach followed in the customization of commercial-off-the-shelf (COTS) modules to make them suitable for the space environment. The environmental tests included random and shock vibration, thermal vacuum cycles in the range −20°C to +40°C and irradiation of the components with a total dose of 1800 rad (18 Gy). The results of the test campaign consolidated the selection of the optical devices for the Exobiology Facility. The spectroscopic performance of the optical layout was tested and benchmarked in comparison with state-of-...
The search for exploitable deposits of water and other volatiles at the Moon’s poles has intensified considerably in recent years, due to the renewed strong interest in lunar exploration. With the return of humans to the lunar surface on the horizon, the use of locally available resources to support long-term and sustainable exploration programs, encompassing both robotic and crewed elements, has moved into focus of public and private actors alike. Our current knowledge about the distribution and concentration of water and other volatiles in the lunar rocks and regolith is, however, too limited to assess the feasibility and economic viability of resource-extraction efforts. On a more fundamental level, we currently lack sufficiently detailed data to fully understand the origins of lunar water and its migration to the polar regions. In this paper, we present LUVMI-X, a mission concept intended to address the shortage of in situ data on volatiles on the Moon that results from a recently concluded design study. Its central element is a compact rover equipped with complementary instrumentation capable of investigating both the surface and shallow subsurface of illuminated and shadowed areas at the lunar south pole. We describe the rover and instrument design, the mission’s operational concept, and a preliminary landing-site analysis. We also discuss how LUVMI-X fits into the diverse landscape of lunar missions under development.
<p>The lunar south pole is of great interest for upcoming lunar exploration endeavors due to the detection of large reservoirs of water ice in the pole&#8217;s permanently shadowed regions [1], which could be utilized to reduce the costs of a sustained presence on the Moon [2]. A strong focus of future robotic exploration missions will therefore be on the detection of water and related volatiles. For this purpose, the project Lunar Volatiles Mobile Instrumentation &#8211; Extended (LUVMI-X) is developing an initial system design as well as payload and mobility breadboards for a small, lightweight rover [3]. One of the proposed payloads is the Volatiles Identification by Laser Analysis instrument (VOILA), which uses laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of the lunar surface with an emphasis on the detection of hydrogen for the inference of the presence of water. VOILA is a joint project by OHB System AG, Laser Zentrum Hannover e.V., and the German Aerospace Center&#8217;s Institute of Optical Sensor Systems. It is designed to analyze targets on the lunar surface in front of the LUVMI-X rover at a variable focus between 300&#160;mm to 500&#160;mm, allowing for precise measurements under various measurement conditions. The spectrometer covers the wavelength range from 350&#160;nm to 790&#160;nm, which includes the hydrogen line at 656.3&#160;nm as well as spectral lines of most major rock-forming elements. The breadboard laboratory setup for VOILA was recently completed and first measurements of Moon-relevant samples have been made. Here, we will show the results of these measurements and will discuss their meaning for the further improvement of the instrument design and for its potential use as a volatile-scouting instrument at the lunar south pole.</p><p>[1] Li S. et al. (2018) PNAS, 36, 8907&#8211;8912. [2] Anand M. et al. (2012) Planet. Space Sci., 74, 42&#8211;48. [3] Gancet J. et al. (2019) ASTRA 2019. [4] Knight A. K. et al. (2000) Appl. Spectrosc., 54, 331&#8211;340. [5] Maurice S. et al. (2012) Space Sci. Rev., 170, 95&#8211;166. [6] Wiens R. C. et al. (2012) Space Sci. Rev., 170, 167&#8211;227. [7] Wiens R. C. et al. (2017) Spectroscopy, 32. [8] Ren X. et al. (2018) EPSC 2018, Abstract EPSC2018-759. [9] Laxmiprasad A. S. et al. (2013) Adv. Space Res., 52, 332&#8211;341. [10] Lasue J. et al. (2012) J. Geophys. Res., 117, E1.</p>
<p><strong>Introduction</strong></p> <p>With the confirmation of water ice in the lunar polar regions [1], the Moon has recently come into the focus of attention of international space agencies again. Volatiles, specifically water and hydrogen, are important resources both for life support and for potential applications as fuels and propellants for spacecraft. In-situ resource utilization (ISRU) of volatiles could significantly reduce the costs of a sustained presence on the Moon and could be beneficial for the future human deep space exploration of the solar system [2]. The detection of volatiles is therefore an important scientific goal for future robotic missions to the Moon.</p> <p>The LUVMI-X project (Lunar Volatiles Mobile Instrumentation &#8211; Extended) is developing an initial system design as well as payload and mobility breadboards for the detection of volatiles in the lunar polar region on a small, lightweight rover [3]. The LUVMI-X rover is shown in Figure 1. One proposed scientific payload is VOILA (Volatiles Identification by Laser Ablation), which is jointly developed by OHB System AG (OHB), Laser Zentrum Hannover (LZH), and the German Aerospace Center&#8217;s Institute of Optical Sensor Systems (DLR-OS). VOILA will use laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of the lunar surface, with a special focus on detecting and quantifying hydrogen and oxygen as indicators for water.</p> <p>LIBS is a versatile technique that requires only optical access to its target [4]. A LIBS spectrum is obtained within seconds, making it well-suited for quick analyses of multiple targets in proximity to the rover. LIBS was first used in space by the ChemCam instrument on board NASA&#8217;s Curiosity rover on Mars [5, 6]. The first LIBS instrument on the Moon was supposed to operate on board the Pragyan rover of India&#8217;s Chandrayaan-2 mission [7]. However, the Chandrayaan-2 lander failed a soft landing in September 2019.</p> <p>Here, we present a summary of the VOILA instrument design and its intended capabilities for volatiles detection at the lunar south pole.</p> <p><img src="data:image/png;base64, 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
Abstract. A highly miniaturized limb sounder for the observation of the O 2 A-Band to derive temperatures in the mesosphere and lower thermosphere is presented. The instrument consists of a monolithic spatial heterodyne spectrometer (SHS), which is able to resolve the rotational structure of the R-branch of that band. The relative intensities of the emission lines follow a Boltzmann distribution and the ratio of the lines can be used to derive the kinetic temperature. The SHS operates at a Littrow wavelength of 761.8 nm and heterodynes a wavelength regime between 761.9 nm and 765.3 nm with a resolving power of
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