BepiColombo - Mission Overview and Science Goals

BepiColombo is a joint mission between the European Space Agency, ESA, and the Japanese Aerospace Exploration Agency, JAXA, to perform a comprehensive exploration of Mercury. Launched on $20^{\mathrm{th}}$ 20 th October 2018 from the European spaceport in Kourou, French Guiana, the spacecraft is now en route to Mercury.Two orbiters have been sent to Mercury and will be put into dedicated, polar orbits around the planet to study the planet and its environment. One orbiter, Mio, is provided by JAXA, and one orbiter, MPO, is provided by ESA. The scientific payload of both spacecraft will provide detailed information necessary to understand the origin and evolution of the planet itself and its surrounding environment. Mercury is the planet closest to the Sun, the only terrestrial planet besides Earth with a self-sustained magnetic field, and the smallest planet in our Solar System. It is a key planet for understanding the evolutionary history of our Solar System and therefore also for the question of how the Earth and our Planetary System were formed.The scientific objectives focus on a global characterization of Mercury through the investigation of its interior, surface, exosphere, and magnetosphere. In addition, instrumentation onboard BepiColombo will be used to test Einstein’s theory of general relativity. Major effort was put into optimizing the scientific return of the mission by defining a payload such that individual measurements can be interrelated and complement each other.

A Comprehensive Approach to On-board Autonomy Verification and Validation

Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we propose a comprehensive approach to on-board autonomy. We rely on model-based reasoning, and we consider many important (on-line and off-line) reasoning capabilities such as plan generation, validation, execution and monitoring, runtime diagnosis, and fault detection, identification, and recovery. The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of our framework, and we have integrated it within an on-board Autonomous Reasoning Engine. Finally, we have evaluated our approach on three case-studies inspired by real-world projects and characterized it in terms of reliability, availability, and performance.

The Marslabor of the University of Basel: Simulation of CLUPI operations in view of the ExoMars 2022 mission

<p>The Close-UP Imager (CLUPI) is one of the instruments of the Rosalind Franklin rover, which will explore Mars in the framework of the ESA/Roscosmos ExoMars mission. CLUPI will be mostly used for acquiring close up-images of geological samples, identifying materials and sedimentary structures that may record information about the hypothetical existence of past extraterrestrial life. Although the technical specifications of CLUPI are well known, it is not possible to readily translate such specifications in terms of feasibility to recognize “textures of interest” at a given distance under specific light conditions on Mars. Accurate predictions are important for taking informed decisions during the tactical planning of the rover. Here, we describe the results of some mission-preparation activities, during which a camera system analogue to CLUPI has been used to photograph rocks samples in an indoor facility (i.e., the Marslabor of the University of Basel) that has been built for simulating a Martian landscape. Under different light conditions, we performed a preliminary assessment of the minimal-working-distance required for interpreting rock textures and sedimentary structures that are potentially present on Mars, including textures that allows for differentiating sedimentary rocks from igneous rocks, grains that allows for classifying sedimentary rocks based on their granulometry, and stromatolitic laminations representing morphological biosignatures. The produced data represents a first step in identifying ideal CLUPI working-distances and illumination, and in preparing an image database that will be of help for optimizing rover operations and the scientific return of  CLUPI during the ExoMars 2022 mission.</p>

Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By

The purpose of this work is to present a novel CubeSat architecture, aimed to explore Near Earth Asteroids. The fast growth in small satellite commercial-off-the-shelf technologies, which characterized the last decade of space industry, is exploited to design a 3U CubeSat able to provide a basic scientific return sufficient to improve the target asteroid dataset. An overview of the current available technologies for each subsystem is presented, followed by a component selection driven by the mission constraints. First a typical asteroid fly-by mission is introduced together with the system and performance requirements. Then each characterizing subsystem is critically analyzed, and the proposed configuration is presented, showing the mission feasibility within only 3.9 kg of wet mass and 385 m/s of total ΔV.

Advantages and Limitations of Current Microgravity Platforms for Space Biology Research

Human Space exploration has created new challenges and new opportunities for science. Reaching beyond the Earth’s surface has raised the issue of the importance of gravity for the development and the physiology of biological systems, while giving scientists the tools to study the mechanisms of response and adaptation to the microgravity environment. As life has evolved under the constant influence of gravity, gravity affects biological systems at a very fundamental level. Owing to limited access to spaceflight platforms, scientists rely heavily on on-ground facilities that reproduce, to a different extent, microgravity or its effects. However, the technical constraints of counterbalancing the gravitational force on Earth add complexity to data interpretation. In-flight experiments are also not without their challenges, including additional stressors, such as cosmic radiation and lack of convection. It is thus extremely important in Space biology to design experiments in a way that maximizes the scientific return and takes into consideration all the variables of the chosen setup, both on-ground or on orbit. This review provides a critical analysis of current ground-based and spaceflight facilities. In particular, the focus was given to experimental design to offer the reader the tools to select the appropriate setup and to appropriately interpret the results.

Cross-NASA divisional relevance of an Ice Giant mission

Robotic space exploration to the outer solar system is difficult and expensive and the space science community works inventively and collaboratively to maximize the scientific return of missions. A mission to either of our solar system Ice Giants, Uranus and Neptune, will provide numerous opportunities to address high-level science objectives relevant to multiple disciplines and deliberate cross-disciplinary mission planning should ideally be woven in from the start. In this review, we recount past successes as well as (NASA-focused) challenges in performing cross-disciplinary science from robotic space exploration missions and detail the opportunities for broad-reaching science objectives from potential future missions to the Ice Giants. This article is part of a discussion meeting issue ‘Future exploration of ice giant systems’.

Compressing combined probes: redshift weights for joint lensing and clustering analyses

ABSTRACT Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. Whilst this strategy significantly improves parameter constraints, decreasing the degeneracies of individual analyses and controlling the systematics, processing data from tens of millions of galaxies is not a trivial task. In this work, we derive and test a new compressed statistic for joint clustering and lensing data analysis, maximizing the scientific return and decreasing the computational cost. Our approach compresses the data by up-weighting the components most sensitive to the parameters of interest, with no loss of information, taking into account information from the cross-correlation between the two probes. We derive optimal redshift weights which may be applied to individual galaxies when testing a given statistic and cosmological model.