The origin of life is one of the most fundamental questions of humanity. It has been and is still being addressed by a wide range of researchers from different fields, with different approaches and ideas as to how it came about. What is still incomplete is constrained information about the environment and the conditions reigning on the Hadean Earth, particularly on the inorganic ingredients available, and the stability and longevity of the various environments suggested as locations for the emergence of life, as well as on the kinetics and rates of the prebiotic steps leading to life. This contribution reviews our current understanding of the geological scene in which life originated on Earth, zooming in specifically on details regarding the environments and timescales available for prebiotic reactions, with the aim of providing experimenters with more specific constraints. Having set the scene, we evoke the still open questions about the origin of life: did life start organically or in mineralogical form? If organically, what was the origin of the organic constituents of life? What came first, metabolism or replication? What was the time-scale for the emergence of life? We conclude that the way forward for prebiotic chemistry is an approach merging geology and chemistry, i.e., far-from-equilibrium, wet-dry cycling (either subaerial exposure or dehydration through chelation to mineral surfaces) of organic reactions occurring repeatedly and iteratively at mineral surfaces under hydrothermal-like conditions.

Laboratory experiments are essential in exploring the mechanisms involved in stardust formation. One key question is how a metal is incorporated into dust for an environment rich in elements involved in stardust formation (C, H, O, Si). To address experimentally this question we have used a radiofrequency cold plasma reactor in which cyclic organosilicon dust formation is observed. Metallic (silver) atoms were injected in the plasma during the dust nucleation phase to study their incorporation in the dust. The experiments show formation of silver nanoparticles (~15 nm) under conditions in which organosilicon dust of size 200 nm or less is grown. The presence of AgSiO bonds, revealed by infrared spectroscopy, suggests the presence of junctions between the metallic nanoparticles and the organosilicon dust. Even after annealing we could not conclude on the formation of silver silicates, emphasizing that most of silver is included in the metallic nanoparticles. The molecular analysis performed by laser mass spectrometry exhibits a complex chemistry leading to a variety of molecules including large hydrocarbons and organometallic species. In order to gain insights into the involved chemical molecular pathways, the reactivity of silver atoms/ions with acetylene was studied in a laser vaporization source. Key organometallic species, Ag CH (n=1-3; m=0-2), were identified and their structures and energetic data computed using density functional theory. This allows us to propose that molecular Ag-C seeds promote the formation of Ag clusters but also catalyze hydrocarbon growth. Throughout the article, we show how the developed methodology can be used to characterize the incorporation of metal atoms both in the molecular and dust phases. The presence of silver species in the plasma was motivated by objectives finding their application in other research fields than astrochemistry. Still, the reported methodology is a demonstration laying down the ground for future studies on metals of astrophysical interest such as iron.

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of "black box" or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.

Global-scale energy flow throughout Earth's magnetosphere is catalyzed by processes that occur at Earth's magnetopause (MP). Magnetic reconnection is one process responsible for solar wind entry into and global convection within the magnetosphere, and the MP location, orientation, and motion have an impact on the dynamics. Statistical studies that focus on these and other MP phenomena and characteristics inherently require MP identification in their event search criteria, a task that can be automated using machine learning so that more man hours can be spent on research and analysis. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network model to detect MP crossings and assist studies of energy transfer into the magnetosphere. As its first application, the LSTM has been implemented into the operational data stream of the Magnetospheric Multiscale (MMS) mission. MMS focuses on the electron diffusion region of reconnection, where electron dynamics break magnetic field lines and plasma is energized. MMS employs automated burst triggers onboard the spacecraft and a Scientist-in-the-Loop (SITL) on the ground to select intervals likely to contain diffusion regions. Only low-resolution survey data is available to the SITL, which is insufficient to resolve electron dynamics. A strategy for the SITL, then, is to select all MP crossings. Of all 219 SITL selections classified as MP crossings during the first five months of model operations, the model predicted 166 (76%) of them, and of all 360 model predictions, 257 (71%) were selected by the SITL. Most predictions that were not classified as MP crossings by the SITL were still MP-like, in that the intervals contained mixed magnetosheath and magnetospheric plasmas. The LSTM model and its predictions are public to ease the burden of arduous event searches involving the MP, including those for EDRs. For MMS, this helps free up mission operation costs by consolidating manual classification processes into automated routines.

Neutron stars (NSs) are extraordinary not only because they are the densest form of matter in the visible Universe but also because they can generate magnetic fields ten orders of magnitude larger than those currently constructed on earth. The combination of extreme gravity with the enormous electromagnetic (EM) fields gives rise to spectacular phenomena like those observed on August 2017 with the merger of a binary neutron star system, an event that generated a gravitational wave (GW) signal, a short -ray burst (sGRB), and a kilonova. This event serves as the highlight so far of the era of multimessenger astronomy. In this review, we present the current state of our theoretical understanding of compact binary mergers containing NSs as gleaned from the latest general relativistic magnetohydrodynamic simulations. Such mergers can lead to events like the one on August 2017, GW170817, and its EM counterparts, GRB 170817 and AT 2017gfo. In addition to exploring the GW emission from binary black hole-neutron star and neutron star-neutron star mergers, we also focus on their counterpart EM signals. In particular, we are interested in identifying the conditions under which a relativistic jet can be launched following these mergers. Such a jet is an essential feature of most sGRB models and provides the main conduit of energy from the central object to the outer radiation regions. Jet properties, including their lifetimes and Poynting luminosities, the effects of the initial magnetic field geometries and spins of the coalescing NSs, as well as their governing equation of state, are discussed. Lastly, we present our current understanding of how the Blandford-Znajek mechanism arises from merger remnants as the trigger for launching jets, if, when and how a horizon is necessary for this mechanism, and the possibility that it can turn on in magnetized neutron ergostars, which contain ergoregions, but no horizons.

With current goals of increased space exploration and travel to Mars, there has been great interest in understanding the long-term effects of high atomic number, high energy (HZE) ion exposure on various organ systems and the immune system. Little is known about late effects on the immune system after high-LET exposure. Therefore, our objective was to determine how natural killer (NK) cell populations were affected in geriatric mice that were exposed to HZE particles during middle-age, thereby representing elderly retired astronauts that undertook deep space missions.

Missions beyond low Earth orbit (LEO) will expose astronauts to ionizing radiation (IR) in the form of solar energetic particles (SEP) and galactic cosmic rays (GCR) including high atomic number and energy (HZE) nuclei. The gastrointestinal (GI) system is documented to be highly radiosensitive with even relatively low dose IR exposures capable of inducing mucosal lesions and disrupting epithelial barrier function. IR is also an established risk factor for colorectal cancer (CRC) with several studies examining long-term GI effects of SEP/GCR exposure using tumor-prone APC mouse models. Studies of acute short-term effects of modeled space radiation exposures in wildtype mouse models are more limited and necessary to better define charged particle-induced GI pathologies and test novel medical countermeasures (MCMs) to promote astronaut safety.

Life on earth is protected from astrophysical cosmic rays by the heliospheric magnetic and slowly varying geomagnetic fields, and by collisions with oxygen and nitrogen molecules in the atmosphere. The collisions generate showers of particles of lesser energy; only muons, a charged particle with a mass between that of an electron and a proton, can reach earth's surface in substantial quantities. Muons are easily detected, used to image interior spaces of pyramids, and known to limit the stability of qubits in quantum computing; yet, despite their charge, average energy of 4 GeV and ionizing properties, muons are not considered to affect chemical reactions or biology. In this Perspective the potential damaging effects of muons on DNA, and hence the repercussions for evolution and disease, are examined. It is argued here that the effect of muons on life through DNA mutations should be considered when investigating the protection provided by the magnetic environment and atmosphere from cosmic rays on earth and exoplanets.