The Venusian interaction with the solar wind leads to the formation of an induced magnetosphere structured by plasma boundaries. Their dynamics is complex, due to the combined influence of external (solar photons, solar wind plasma and interplanetary magnetic field (IMF)) and internal (ionized atmosphere) drivers. Studying these drivers helps understanding the transfer of energy and momentum throughout the Venusian system, and has thus implications for the erosion of the atmosphere through its coupling with the solar wind. We here analyze and rank the influence of the main drivers of the Venusian bow shock and ion composition boundary locations. We revisit the results by Signoles et al. (2023) based on Venus Express measurements by combining several methods such as the Akaike Information Criterion, Least Absolute Shrinkage Selection Operator regression, and partial correlations. These methods allow to investigate cross correlations that appear and can bias the interpretation, and allow to rank drivers with robust approaches. The bow shock appears primarily driven by the IMF intensity or Mach number, the IMF θ_bn angle separating quasi-perpendicular vs quasi-parallel shocks, and then the solar extreme ultraviolet fluxes and solar wind dynamic pressure (with little influence of the convective electric field induced asymmetries). The Ion Composition Boundary is primarily driven by extreme ultraviolet fluxes, with a more reduced influence of several solar wind parameters and IMF induced magnetic pileup asymmetries. We also compare the behaviors of both boundaries and then compare the bow shock driver rankings at Mars and Venus. Finally we propose an analysis of the drivers of the extreme bow shock and ion composition boundary excursions.

Global dust storms at Mars have a significant impact on the atmosphere and ionosphere, but only recently has their impact on the magnetosphere been investigated. The 2018 global dust storm at Mars was the first global event following the arrival of the Mars Atmosphere and Volatile EvolutioN mission at Mars, providing additional data to that of Mars Express observing how plasma boundaries (the bow shock and induced magnetospheric boundary (IMB)) varied over the storm duration. Applying 2D boundary models to spacecraft crossings, we find that the variability of both boundaries increases due to the storm onset and continues after surface conditions have returned to normal. While the bow shock shows no influence from the crustal magnetic fields in the southern hemisphere of Mars, the IMB dips here, the opposite of its normal bulging behavior. This is consistent with the signature found during the 2007 global dust storm. We suggest this signature is due to a decrease in ionospheric pressure in this area from the transport of ionized particles down crustal magnetic field lines toward the surface of Mars.

Dust and gas outbursts are recurrent cometary phenomena, playing a crucial role in shaping the coma. Proposed outburst trigger mechanisms include cliff collapse, pressure pockets, and amorphous-to-crystalline phase transition of water ice; however, the underlying processes remain inadequately understood. In this study, we analyse Rosetta/ROSINA data from multiple outbursts on comet 67P/Churyumov-Gerasimenko and present the evolution of the gas composition in the comet's coma during outburst events. We distinguish two distinct categories of cometary outbursts on the comet: water-driven events characterized by rapid (minutes to hours) changes in coma composition, and CO2-driven events displaying a slow, prolonged (hours to days) increase in highly volatile species. We tentatively associate these different gas composition patterns with different trigger mechanisms. Exposure of fresh ice due to cliff collapse leads to a notable water enhancement, while most perihelion outbursts coincide with substantial density increases of CO2. We propose that these CO2-driven events originate from subsurface gas-filled cavities, whose walls are suggested to have been sealed by earlier refreezing of CO2 migrating from warmer spots, hence increasing the cavity pressure required to burst.

There is ample evidence for magnetic reconnection in the solar system, but it is a nontrivial task to visualize, to determine the proper approaches and frames to study, and in turn to elucidate the physical processes at work in reconnection regions from in-situ measurements of plasma particles and electromagnetic fields. Here an overview is given of a variety of single- and multi-spacecraft data analysis techniques that are key to revealing the context of in-situ observations of magnetic reconnection in space and for detecting and analyzing the diffusion regions where ions and/or electrons are demagnetized. We focus on recent advances in the era of the Magnetospheric Multiscale mission, which has made electron-scale, multi-point measurements of magnetic reconnection in and around Earth's magnetosphere.

Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA’s F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum V capability of 600 ms^-1 . Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes – B1, provided by the Japanese space agency, JAXA, and B2 – that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission’s science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule.

Mars's magnetosphere is a sensitive system, varying due to external and internal factors, such as solar wind conditions and crustal magnetic fields. A signature of this influence can be seen in the position of two boundaries; the bow shock and the induced magnetospheric boundary (IMB). The bow shock moves closer to Mars during times of high solar activity, and both the bow shock and IMB bulge away from Mars over crustal magnetic fields in the southern hemisphere. This study investigates whether large-scale atmospheric events at Mars have any signature in these two magnetic boundaries, by investigating the 2007 storm. The 2007 global storm lasted for several months and increased atmospheric temperatures and densities of both water vapor and carbon dioxide in the atmosphere, leading to an increase in atmospheric escape. Using Mars Express, we identified boundary locations before, during, and after the event, and compared these to modeled boundary locations and areographical locations on Mars. We find that, while it is unclear whether the bow shock position is impacted by the storm, the IMB location does change significantly, despite the orbital bias introduced by Mars Express. The terminator distance for the IMB peaks at longitudes 0°–40° and 310°–360°, leaving a depression around 180° longitude, where the boundary usually extends to higher altitudes due to the crustal magnetic fields. We suggest this may be due to the confinement of ionospheric plasma over crustal fields preventing mixing with the dust, creating a dip in ionospheric pressure here.

An unusually low density solar wind event was observed in December 2022 moving past both Earth and Mars. The source was traced back to a coronal hole and active region on the Sun's surface. The resulting solar wind lead to the development of a co-rotating interaction region (CIR) and trailing rarefaction region that lasted for multiple solar rotations. Within this structure, the solar wind conditions, including density, velocity, and magnetic field magnitude and orientation drastically changed. In this study we analyze the response of the Martian ionosphere using MAVEN data to these changing solar wind conditions. The low density solar wind region associated with the December event resulted in the expansion of the Martian ionospheric boundaries. We show that the ion composition boundary (ICB) is located at extreme altitudes that are beyond previously observed locations from the MAVEN mission between 2015 and 2018. Furthermore, the boundary between shocked solar wind and the Martian ionosphere identified using electron and ion data moved together on the dayside of the planet with the changing solar wind conditions. However, at the flank region these boundaries do not move together, and we show here that the decoupling of the two boundaries may be the result of a change in the interplanetary magnetic field azimuthal angle. The Sun constantly emits fast moving charged particles into interplanetary space in what is known as the solar wind. In December 2022, a region of fast moving solar wind overtook a region of slower moving solar wind. The fast solar wind acted as a broom sweeping up and compressing the slower wind preceding it. This interaction of two solar wind speeds is observed to rotate with the Sun. Trailing this interaction region is a region of low density solar wind. In December 2022, this type of interplanetary solar wind structure interacted with Mars' electrically charged atmosphere, or ionosphere. Throughout this period, the ionosphere boundary characterized by a transition from solar wind ions to planetary ions moved up and down relative to the planet near dusk. As the rarefaction region passed through the system, the ionosphere expanded to unusually high heights as it was not compressed by the solar wind. However, the flapping of the ionospheric boundary seems to depend on the orientation that the solar wind interacts with Mars. The disappearing solar wind event observed at Mars in December 2022 was caused by a co-rotating interaction region Mars' ionosphere expanded further than previously observed locations between 2015 through 2018 The flank ion composition boundary experiences large fluctuations in altitude after a change in the interplanetary magnetic field direction

Astronomical observatories have been identified as substantial contributors to the carbon footprint of astrophysical research. Being part of the collaboration that is currently developing the medium-sized telescopes in the Cherenkov Telescope Array, a ground-based observatory for very-high-energy gamma-rays that will comprise 64 telescopes deployed on two sites, we assessed the environmental impacts of one medium-sized telescope on the northern site with a life-cycle assessment. We identified resource use and climate change as having the most significant impacts due to telescope manufacturing and energy consumption during operations. We estimate life-cycle greenhouse gas emissions of 2,660 +/- 274 tCO2e for the telescope, 44% of which arise from construction, 1% from on-site assembly and commissioning and 55% from operations over 30 yr. The environmental impacts could be reduced by using renewable energy during construction and operations, using fewer electronic components and metal casting and using recycled materials. We propose complementing the project requirements with environmental budgets as an effective measure for impact management and reductions. According to a life-cycle analysis, the main environmental impacts of building and operating an astronomical telescope are greenhouse gas emissions and the use of mineral resources. Impacts can be reduced by using renewable energy and by making design changes.

Research infrastructures have been identified as an important source of greenhouse gas emissions of astronomical research. Based on a comprehensive inventory of 1,211 ground-based observatories and space missions, we assessed the evolution of the number of astronomical facilities and their carbon footprint from 1945 to 2022. We found that space missions dominate greenhouse gas emissions in astronomy, showing an important peak at the end of the 1960s, followed by a decrease that has turned again into a rise over the last decade. Extrapolating past trends, we predict that greenhouse gas emissions from astronomical facilities will experience no strong decline in the future, and may even rise substantially, unless research practices are changed. We demonstrate that a continuing growth in the number of operating astronomical facilities is not environmentally sustainable. These findings should motivate the astronomical community to reflect about the necessary evolutions that would put astronomical research on a sustainable path. A comprehensive inventory of the carbon footprints of 1,211 ground-based astronomical observatories and space missions over a period of 78 years is used to model the expected future annual carbon footprint from astronomical research infrastructures.

In December 2022, an extremely low-density solar wind stream encountered first the Earth and then Mars, shortly after the radial alignment of the two planets (i.e., Mars opposition). As the low-density stream passed Mars, the properties of the Mars-solar wind interaction changed dramatically in response to the low solar wind Alfvenic Mach number and dynamic pressure. The Martian magnetosphere and its boundaries expanded by thousands of kilometers, extending outside of the nominal average bow shock location. The low upstream Mach number resulted in a low ratio of plasma to magnetic field pressure in the magnetosheath, allowing the formation of a very high but stable ion temperature anisotropy, together with a very low level of electromagnetic instabilities. Meanwhile, the decrease in solar wind dynamic pressure caused the Martian upper ionosphere at the terminator to transition from a magnetized state to an unmagnetized state. This event provides an opportunity to study a unique end-member state of the Mars-solar wind interaction. The solar wind consists of a supersonic flow of hot ionized gas (plasma) from the Sun that flows outward through the solar system. The solar wind interacts with planetary atmospheres and magnetic fields, which form shielded regions known as magnetospheres. The characteristics of the solar wind, including its density and speed, vary over a wide range. The shape, extent, and physical characteristics of planetary magnetospheres in turn respond to these changes in the solar wind. In this work, we study the response of the Martian magnetosphere to an extremely low-density solar wind stream. During the passage of this stream, the Martian magnetosphere expanded by thousands of kilometers, and its physical characteristics changed dramatically. This event reveals a unique outlier state for the Mars-solar wind interaction. An extremely low-density solar wind stream encountered the Earth and Mars just after Mars opposition in December 2022The Martian magnetosheath developed very unusual properties, with a low level of plasma instabilities and a high ion anisotropyThe Martian magnetosphere and its boundaries expanded by thousands of km, extending outside of the location of the nominal bow shock

In our previously published work, we introduced a supervised deep learning method for event detection in multivariate time series data, employing regression instead of binary classification. This simplification avoids the need for point-wise labels throughout the entire dataset, relying solely on ground truth events defined as time points or intervals. In this paper, we establish mathematically that our method is universal, and capable of detecting any type of event with arbitrary precision under mild continuity assumptions on the time series. These events may encompass change points, frauds, anomalies, physical occurrences, and more. We substantiate our theoretical results using the universal approximation theorem for feed-forward neural networks (FFN). Additionally, we provide empirical validations that confirm our claims, demonstrating that our method, with a limited number of parameters, outperforms other deep learning approaches, particularly for rare events and imbalanced datasets from different domains.

Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predicting future trends. Despite extensive research exploring diverse methods for event detection in time series, with deep learning approaches being among the most advanced, there is still room for improvement and innovation in this field. In this paper, we present a new deep learning supervised method for detecting events in multivariate time series data. Our method combines four distinct novelties compared to existing deep-learning supervised methods. Firstly, it is based on regression instead of binary classification. Secondly, it does not require labeled datasets where each point is labeled; instead, it only requires reference events defined as time points or intervals of time. Thirdly, it is designed to be robust by using a stacked ensemble learning meta-model that combines deep learning models, ranging from classic feed-forward neural networks (FFNs) to state-of-the-art architectures like transformers. This ensemble approach can mitigate individual model weaknesses and biases, resulting in more robust predictions. Finally, to facilitate practical implementation, we have developed a Python package to accompany our proposed method. The package, called eventdetector-ts, can be installed through the Python Package Index (PyPI). In this paper, we present our method and provide a comprehensive guide on the usage of the package. We showcase its versatility and effectiveness through different real-world use cases from natural language processing (NLP) to financial security domains.

Event detection in time series is a challenging task due to the prevalence of imbalanced datasets, rare events, and time interval-defined events. Traditional supervised deep learning methods primarily employ binary classification, where each time step is assigned a binary label indicating the presence or absence of an event. However, these methods struggle to handle these specific scenarios effectively. To address these limitations, we propose a novel supervised regression-based deep learning approach that offers several advantages over classification-based methods. Our approach, with a limited number of parameters, can effectively handle various types of events within a unified framework, including rare events and imbalanced datasets. We provide theoretical justifications for its universality and precision and demonstrate its superior performance across diverse domains, particularly for rare events and imbalanced datasets.

Event detection in time series data is a crucial task spanning various domains, and extensive research has explored methods to achieve this goal. These methods range from traditional threshold-based techniques to more advanced deep learning approaches. However, a comprehensive survey of existing methods reveals that each approach has its limitations, often lacking mathematical validation and exhibiting limited robustness. To address these limitations, this paper introduces a novel framework rooted in universal approximation theory, a well-established and proven methodology. This framework showcases the capability to accurately detect a broad spectrum of events in multivariate time series data with the desired precision. To bolster robustness, the proposed framework employs a stacked ensemble learning meta-model, effectively mitigating individual model weaknesses and biases, thereby yielding more resilient predictions. Moreover, this paper provides a user-friendly quick-start guide for implementing the framework, demonstrated using two diverse datasets: one from the field of planetary science and another from financial security. The results underscore the effectiveness of the proposed framework in achieving high accuracy and precision when detecting events in both datasets. In summary, this paper presents a novel and robust framework for event detection in time series data, founded on universal approximation theory and bolstered by ensemble learning.

The Lunar Orbital Platform - Gateway (LOP - Gateway, or simply Gateway) is a crewed platform that will be assembled and operated in the vicinity of the Moon by NASA and international partner organizations, including ESA, starting from the mid-2020s. It will offer new opportunities for fundamental and applied scientific research. The Moon is a unique location to study the deep space plasma environment. Moreover, the lunar surface and the surface-bounded exosphere are interacting with this environment, constituting a complex multi-scale interacting system. This paper examines the opportunities provided by externally mounted payloads on the Gateway in the field of space plasma physics, heliophysics and space weather, and also examines the impact of the space environment on an inhabited platform in the vicinity of the Moon. It then presents the conceptual design of a model payload, required to perform these space plasma measurements and observations. It results that the Gateway is very well-suited for space plasma physics research. It allows a series of scientific objectives with a multi-disciplinary dimension to be addressed.

We present an assessment of the greenhouse gases emissions of the Institute for Research in Astrophysics and Planetology (IRAP), located in Toulouse (France). It was performed following the established"Bilan Carbone"methodology, over a large scope compared to similar previous studies, including in particular the contribution from the purchase of goods and services as well as IRAP's use of external research infrastructures, such as ground-based observatories and space-borne facilities. The carbon footprint of the institute for the reference year 2019 is 7400 +/- 900 tCO2e. If we exclude the contribution from external research infrastructures to focus on a restricted perimeter over which the institute has some operational control, IRAP's emissions in 2019 amounted to 3300 +/- 400 tCO2e. Over the restricted perimeter, the contribution from purchasing goods and services is dominant, about 40% of the total, slightly exceeding the contribution from professional travel including hotel stays, which accounts for 38%. Local infrastructures make a smaller contribution to IRAP's carbon footprint, about 25% over the restricted perimeter. We note that this repartition may be specific to IRAP, since the energy used to produce the electricity and heating has a relatively low carbon footprint. Over the full perimeter, the large share from the use of ground-based observatories and space-borne facilities and the fact that the majority of IRAP purchases are related to instrument development indicate that research infrastructures represent the most significant challenge for reducing the carbon footprint of research at our institute. With ~260 staff members employed, our results imply that performing research in astronomy and astrophysics at IRAP according to the standards of 2019 produces average GHG emissions of 28 tCO2e/yr per person involved in that activity (Abridged).

Global dust storms at Mars engulf the entire planet in a dusty haze, causing increases in temperature and ion escape as dust is lifted up to 80 km in altitude. The two most recent storms occurred in 2007 (Mars Year (MY) 28) and 2018 (MY34), and have been observed by spacecrafts such as Mars Express (MEx). MEx has been operating at Mars since 2004, and has produced a long time-base of plasma measurements from as low as 250 km. Using MEx, we investigate whether the 2007 dust storm has influenced the magnetosphere of Mars by looking at the position of the bow shock and induced magnetospheric boundary, compared to the expected position provided by 3D magnetohydrodynamical models. To identify boundary positions, we use data from the ASPERA-3 instrument (Analyser of Space Plasma and EneRgetic Atoms) onboard MEx, which contains an electron spectrometer (ELS), ion mass analyser (IMA), neutral particle imager (NPI) and neutral particle detector (NPD). For this study, we use data from ELS and IMA. We consider a number of influences on the boundary position, including the solar wind conditions and the crustal fields. Our study period includes time before, during, and after the MY28 global storm, and we expected the bow shock and induced magnetospheric boundary to increase in altitude due to the storm. Out results show that the system is more complex, and multiple influences need to be distinguished to leave any change due to the dust storm itself.

Enceladus, an icy moon of Saturn, possesses an internal water ocean and jets expelling ocean material into space. Cassini investigations indicated that the subsurface ocean could be a habitable environment having a complex interaction with the rocky core. Further investigation of the composition of the plume formed by the jets is necessary to fully understand the ocean, its potential habitability, and what it tells us about Enceladus’s origin. Moonraker has been proposed as an ESA M-class mission designed to orbit Saturn and perform multiple flybys of Enceladus, focusing on traversals of the plume. The proposed Moonraker mission consists of an ESA-provided platform with strong heritage from JUICE and Mars Sample Return and carrying a suite of instruments dedicated to plume and surface analysis. The nominal Moonraker mission has a duration of ∼13.5 yr. It includes a 23-flyby segment with 189 days allocated for the science phase and can be expanded with additional segments if resources allow. The mission concept consists of investigating (i) the habitability conditions of present-day Enceladus and its internal ocean, (ii) the mechanisms at play for the communication between the internal ocean and the surface of the South Polar Terrain, and (iii) the formation conditions of the moon. Moonraker, thanks to state-of-the-art instruments representing a significant improvement over Cassini's payload, would quantify the abundance of key species in the plume, isotopic ratios, and the physical parameters of the plume and the surface. Such a mission would pave the way for a possible future landed mission.

The development and use of research infrastructures accounts for more than 70% of the carbon footprint of the Institute for Research in Astrophysics and Planetology. Our community needs to rethink this crucial facet of astronomical research to engage in effective and perennial reduction strategies.

The carbon footprint of astronomical research is an increasingly topical issue with first estimates of research institute and national community footprints having recently been published. As these assessments have typically excluded the contribution of astronomical research infrastructures, we complement these studies by providing an estimate of the contribution of astronomical space missions and ground-based observatories using greenhouse gas emission factors that relates cost and payload mass to carbon footprint. We find that worldwide active astronomical research infrastructures currently have a carbon footprint of 20.3 ± 3.3 MtCO2 equivalent (CO2e) and an annual emission of 1,169 ± 249 ktCO2e yr−1 corresponding to a footprint of 36.6 ± 14.0 tCO2e per year per astronomer. Compared with contributions from other aspects of astronomy research activity, our results suggest that research infrastructures make the single largest contribution to the carbon footprint of an astronomer. We discuss the limitations and uncertainties of our method and explore measures that can bring greenhouse gas emissions from astronomical research infrastructures towards a sustainable level. Astronomical research facilities, such as space telescopes, space probes or ground-based observatories, are the largest contributor to an astronomer’s carbon footprint, well beyond other activities such as flying to conferences or running computer simulations.