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Many space-based and ground-based telescope proposals are trending towards larger primary mirror aperture diameters, driven in part by the desire within the astrophysics community to discover Earth-like exoplanets. Though ground-based telescopes can continue to grow in size, space-based telescopes are limited by the fairing size of a single launch vehicle. To enable larger and larger space-based telescopes, on-orbit assembly must be considered. This work seeks to understand the impact of flexible design options on the upfront and long-term costs of an in-space assembled telescope mission by evaluating space telescope architectures that incorporate different launch platforms. A 20-meter telescope concept is analyzed, and models of the structural, optical, thermal, launch, and trajectory subsystems are used to explore the impacts of flexible design on the launch costs and relative, or comparative, complexity of the telescope. The effects of uncertainty in the launch module are explored, and flexible design concepts are analyzed to identify alternate design concepts that were more favorable, in terms of estimated cost and complexity, once uncertainty is considered. The results of this analysis indicate that design concepts that incorporate flexibility in both the scope and timing of in-space telescope architectures, particularly those that use heritage design aspects from existing telescope missions, should be explored early in the concept development phase and may present better alternatives to existing in-space assembled telescope concepts., National Aeronautics and Space Administration (NASA)

37th International Electric Propulsion Conference, Cambridge, Massachusetts, Electrosprays are a promising technology for highly effective propulsion for CubeSats and other small satellites. In this work we present the design, initial operations, and performance measurements of the latest generation of ion Electrospray Propulsion System (iEPS). Choosing the best ionic liquid propellant is discussed, where we note that the common EMI-BF4 tends to absorb large amounts of water from the atmosphere. We suggest instead to use EMI-CF3BF3, which hardly absorbs water and performs as well or better than EMI-BF4 in our results. The thrusters tested here produce around 12 micronewtons of thrust at a specific impulse of around 600 seconds, while only consuming 120 milliwatts of power., National Aeronautics and Space Administration (NASA)

Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or parameterized input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. Statistical model checking (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn surrogate models, and uses conformal inference to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We compare this prediction interval with the interval we get using risk estimation procedures. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.

Proper elements represent a dynamical fingerprint of an object’s inherent state and have been used by small-body taxonomists in characterizing asteroid families. Being linked to the underlying dynamical structure of orbits, Celletti, Pucacco, and Vartolomei have recently adopted these innate orbital parameters for the association of debris from breakup or collision into its parent satellite. Building from this rich astronomical heritage and recent foundations, we introduce an unsupervised learning method—density-based spatial clustering of applications with noise (DBSCAN)—to determine clusters of orbital debris in the space of proper elements. Data is taken from the space-object catalog of trackable Earth-orbiting objects in the form of two-line element sets. Proper elements for debris fragments in low-Earth orbit are computed using an ad hoc numerical scheme, akin to the state-of-the-art Fourier-series-based synthetic method for the asteroid domain. Given the heuristic nature of classical DBSCAN, we investigate the use of neural networks, trained on known families, to augment DBSCAN into a classification problem and apply it to analyst objects of unknown origin.

Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery of the individual images. Although some recent (variational) methods have demonstrated high-resolution image recovery based on jointly recovering sequential images, there remain robustness issues due to parameter tuning and restrictions on the type of sequential images. Here, we present a method based on hierarchical Bayesian learning for the joint recovery of sequential images that incorporates prior intra- and inter-image information. Our method restores the missing information in each image by “borrowing” it from the other images. More precisely, we couple sequential images by penalizing their pixel-wise difference. The corresponding penalty terms (one for each pixel and pair of subsequent images) are treated as weakly-informative random variables that favor small pixel-wise differences but allow occasional outliers. As a result, all of the individual reconstructions yield improved accuracy. Our method can be used for various data acquisitions and allows for uncertainty quantification. Some preliminary results indicate its potential use for sequential deblurring and magnetic resonance imaging.

Integrating thinking skills into higher education pedagogy requires suitable models, methods, and tools for both instruction and assessment. Some of these tools apply one or more educational technologies. The articles in this special issue focus on higher education with four common themes: online or virtual courses and modules, science and engineering education, active learning methods, and critical thinking. This special issue sheds light on the critical importance of thinking skills development and assessment in higher education and underscores the need for continued research and innovation in the realm of educational technology.

Machine learning (ML) enables the development of interatomic potentials with the accuracy of first principles methods while retaining the speed and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks map descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor Li $$_{10}$$ 10 Ge(PS $$_{6}$$ 6 ) $$_{2}$$ 2 (LGPS). We further investigate how these errors affect simulated properties and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Graphical abstract

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This paper proposes a generalized self-cueing real-time attention scheduling framework for DNN-based visual machine perception pipelines on resource-limited embedded platforms. Self-cueing means we identify subframe-level regions of interest in a scene internally by exploiting temporal correlations among successive video frames as opposed to externally via a cueing sensor. One limitation of our original self-cueing-and-inspection strategy (Liu et al. in Proceedings of the 28th IEEE real-time and embedded technology and applications symposium (RTAS), 2022b) lies in its lack of computational efficiency under high workloads, like busy traffic scenarios where a large number of objects are identified and separately inspected. We extend the conference publication by integrating image resizing with intermittent inspection and task batching in attention scheduling. The extension enhances the original algorithm by accelerating the processing of large objects by reducing their resolution at the cost of only a negligible degradation in accuracy, thereby achieving a higher overall object inspection throughput. After extracting partial regions around objects of interest, using an optical flow-based tracking algorithm, we allocate computation resources (i.e. DNN inspection) to them in a criticality-aware manner using a generalized batched proportional balancing algorithm (GBPB), to minimize a concept of generalized system uncertainty. It saves computational resources by inspecting low-priority regions intermittently at low frequencies and inspecting large objects at low resolutions. We implement the system on an NVIDIA Jetson Xavier platform and extensively evaluate its performance using a real-world driving dataset from Waymo. The proposed GBPB algorithm consistently outperforms the previous BPB algorithm that only uses intermittent inspection and a set of baselines. The performance gain of GBPB is larger in facing more significant resource constraints (i.e., lower samp

In this paper, we propose a set of guidelines to select a solver for the solution of nonlinear programming problems. We conduct a comparative analysis of the convergence performances of commonly used solvers for both unconstrained and constrained nonlinear programming problems. The comparison metrics involve accuracy, convergence rate, and computational time. MATLAB is chosen as the implementation platform due to its widespread adoption in academia and industry. Our study includes solvers which are either freely available or require a license, or are extensively documented in the literature. Moreover, we differentiate solvers if they allow the selection of different optimal search methods. We assess the performance of 24 algorithms on a set of 60 benchmark problems. We also evaluate the capability of each solver to tackle two large-scale UAV optimal path planning scenarios, specifically the 3D minimum time problem for UAV landing and the 3D minimum time problem for UAV formation flying. To enrich our analysis, we discuss the effects of each solver’s inner settings on accuracy, convergence rate, and computational time.

The global optimization literature places large emphasis on reducing intractable optimization problems into more tractable structured optimization forms. In order to achieve this goal, many existing methods are restricted to optimization over explicit constraints and objectives that use a subset of possible mathematical primitives. These are limiting in real-world contexts where more general explicit and black box constraints appear. Leveraging the dramatic speed improvements in mixed-integer optimization (MIO) and recent research in machine learning, we propose a new method to learn MIO-compatible approximations of global optimization problems using optimal decision trees with hyperplanes (OCT-Hs). This constraint learning approach only requires a bounded variable domain, and can address both explicit and inexplicit constraints. We solve the MIO approximation to find a near-optimal, near-feasible solution to the global optimization problem. We further improve the solution using a series of projected gradient descent iterations. We test the method on numerical benchmarks from the literature as well as real-world design problems, demonstrating its promise in finding global optima efficiently.

We consider solving high-order and tight semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solutions. Instead of solving the SDP alone, we propose a new algorithmic framework that blends local search using the nonconvex POP into global descent using the convex SDP. In particular, we first design a globally convergent inexact projected gradient method (iPGM) for solving the SDP that serves as the backbone of our framework. We then accelerate iPGM by taking long, but safeguarded, rank-one steps generated by fast nonlinear programming algorithms. We prove that the new framework is still globally convergent for solving the SDP. To solve the iPGM subproblem of projecting a given point onto the feasible set of the SDP, we design a two-phase algorithm with phase one using a symmetric Gauss–Seidel based accelerated proximal gradient method (sGS-APG) to generate a good initial point, and phase two using a modified limited-memory BFGS (L-BFGS) method to obtain an accurate solution. We analyze the convergence for both phases and establish a novel global convergence result for the modified L-BFGS that does not require the objective function to be twice continuously differentiable. We conduct numerical experiments for solving second-order SDP relaxations arising from a diverse set of POPs. Our framework demonstrates state-of-the-art efficiency, scalability, and robustness in solving degenerate SDPs to high accuracy, even in the presence of millions of equality constraints.

Soft adhesive contacts are ubiquitous in nature and are increasingly used in synthetic systems, such as flexible electronics and soft robots, due to their advantages over traditional joining techniques. While methods to study the failure of adhesives typically apply tensile loads to the adhesive joint, less is known about the performance of soft adhesives under shear and torsion, which may become important in engineering applications. A major challenge that has hindered the characterization of shear/torsion-induced delamination is imposed by the fact that, even after delamination, contact with the substrate is maintained, thus allowing for frictional sliding and re-adhesion. In this work, we address this gap by studying the controlled delamination of soft cylinders under combined compression and torsion. Our experimental observations expose the nucleation of delamination at an imperfection and its propagation along the circumference of the cylinder. The observed sequence of ‘stick-slip’ events and the sensitivity of the delamination process to material parameters are explained by a theoretical model that captures axisymmetric delamination patterns, along with the subsequent frictional sliding and re-adhesion. By opening up an avenue for improved characterization of adhesive failure, our experimental approach and theoretical framework can guide the design of adhesives in future applications.

Department of Defense (DoD), National Science Foundation (NSF)

Abstract Cavitation has long been recognized as a crucial predictor, or precursor, to the ultimate failure of various materials, ranging from ductile metals to soft and biological materials. Traditionally, cavitation in solids is defined as an unstable expansion of a void or a defect within a material. The critical applied load needed to trigger this instability -- the critical pressure -- is a lengthscale independent material property and has been predicted by numerous theoretical studies for a breadth of constitutive models. While these studies usually assume that cavitation initiates from defects in the bulk of an otherwise homogeneous medium, an alternative and potentially more ubiquitous scenario can occur if the defects are found at interfaces between two distinct media within the body. Such interfaces are becoming increasingly common in modern materials with the use of multimaterial composites and layer-by-layer additive manufacturing methods. However, a criterion to determine the threshold for interfacial failure, in analogy to the bulk cavitation limit, has yet to be reported. In this work, we fill this gap. Our theoretical model captures a lengthscale independent limit for interfacial cavitation, and is shown to agree with our observations at two distinct lengthscales, via two different experimental systems. To further understand the competition between the two cavitation modes (bulk versus interface), we expand our investigation beyond the elastic response to understand the ensuing unstable propagation of delamination at the interface. A phase diagram summarizes these results, showing regimes in which interfacial failure becomes the dominant mechanism.

With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered.

MIT Media Lab Consortium

Adaptive optics (AO) compensation for imaging or coherent illumination of a remote object relies on accurate sensing of atmospheric aberrations. When a coherent beacon is projected onto the object to enable wavefront sensing, the reflected reference wave will exhibit random variation in phase and amplitude characteristics of laser speckle. In a Shack–Hartmann wavefront sensor (SHWFS) measurement, speckle effects cause fluctuations in the intensity of focal spots and errors in the position of their centroids relative to those expected from purely atmospheric phase aberrations. The resulting error in wavefront measurements negatively impacts the quality of atmospheric phase conjugation. This paper characterizes the effect of reflected laser speckle on the accuracy of SHWFS measurements for ground-to-space beam projection systems in weak turbulence conditions. We show via simulation that the speckle-induced error in centroiding depends on the ratio between beacon diameter and the diffraction-limited resolution of the lenslet and confirm these results with experimental data. We provide experimental validation that averaging of SHWFS lenslet spot intensities over speckle realizations converges to the incoherent intensity as expected. We further show that the effects of shot noise and speckle noise add in quadrature, simplifying noise analysis. Finally, we characterize the effect of temporal averaging under typical conditions of target motion and integration time. This work provides a straightforward set of relations that can help investigators more accurately estimate the required integration time for wavefront sensing in the presence of laser speckle.

The use of numerical simulation for prediction of characteristics of chaotic dynamical systems inherently involves unpredictable processes. In this work, we develop a model for the expected error in the simulation of ergodic, chaotic ordinary differential equation (ODE) systems, which allows for discretization and statistical effects due to unpredictability. Using this model, we then generate a framework for understanding the relationship between the sampling cost of a simulation and the expected error in the result and explore the implications of the various parameters of simulations. Finally, we generalize the framework to consider the total cost—including unsampled spin-up timesteps—of simulations and consider the implications of parallel computational environments to give a realistic model of the relationship between wall-clock time and the expected error in simulation of a chaotic ODE system.

Ionic-liquid ion sources produce beams of charged particles through evaporation and acceleration of ions and charged droplets from the surface of an ionic liquid. The composition of the emitted beam can impact the performance of ion sources for various applications such as focused beams for microfabrication and space propulsion. Numerical inference is considered for quantification of the beam composition of an ionic-liquid ion source through determining the current fraction of different species along with providing uncertainty in inferred values. An analysis of previously presented data demonstrates the ability to quantify the presence of ion clusters, including the distinct presence of heavy ion clusters such as heptamers. Quantification of beam composition will be an important technique for quantitative comparison of different time-of-flight data.

Remote robot manipulation with human control enables applications where safety and environmental constraints are adverse to humans (e.g. underwater, space robotics and disaster response) or the complexity of the task demands human-level cognition and dexterity (e.g. robotic surgery and manufacturing). These systems typically use direct teleoperation at the motion level, and are usually limited to low-DOF arms and 2D perception. Improving dexterity and situational awareness demands new interaction and planning workflows. We explore the use of human-robot teaming through teleautonomy with assisted planning for remote control of a dual-arm dexterous robot for multi-step manipulation, and conduct a within-subjects experimental assessment (n=12 expert users) to compare it with direct teleoperation with an imitation controller with 2D and 3D perception, as well as teleoperation through a teleautonomy interface. The proposed assisted planning approach achieves task times comparable with direct teleoperation while improving other objective and subjective metrics, including re-grasps, collisions, and TLX workload. Assisted planning in the teleautonomy interface achieves faster task execution, and removes a significant interaction with the operator's expertise level, resulting in a performance equalizer across users. Our study protocol, metrics and models for statistical analysis might also serve as a general benchmarking framework in teleoperation domains.

With the introduction of IATA’s New Distribution Capability (NDC), airlines will no longer be limited to discrete fare classes for their fare product distribution but could show fare quotes from continuous ranges to booking requests. NDC will also allow airlines to present different fare quotes to passengers from different demand segments as identified by the airline. In theory, airlines can better extract passenger willingness to pay, and thus, see gains in revenue, by offering segmented continuous fare quotes to different passengers requesting to book. This paper describes the revenue management (RM) methods for Segmented Continuous Pricing and examines their potential effects on airlines’ revenue through simulations in the Passenger Origin–Destination Simulator (PODS). We describe a class-based algorithm for continuous pricing, a straightforward extension from the traditional methods used with existing RM systems. Our simulation results show that in a calibrated scenario in which only one airline adopts Segmented Continuous Pricing and has an 80% accuracy in identifying business versus leisure passenger booking requests, the first-mover airline can see as much as a 17% revenue gain, at the expense of competitors. The revenue gains come primarily from the leisure passenger segment by offering lower fares than competitors closer to departure. The first-mover airline loses bookings but does not see losses in revenue from the business passenger segment. We also explore potential response strategies by the competing airlines. We discover that competitors can reverse the first-mover’s revenue gain by removing their fare restrictions while still using traditional RM methods. We conclude that although adopting Segmented Continuous Pricing is promising in theory, its gains in practice will depend heavily on the competitive situation and the responses made by competing airlines.

National Aeronautics and Space Administration (NASA)

Intense surface eruptions are observed along the curved surface of a confined cylindrical film of hydrogel subject to laser-induced converging-diverging shock loading. Detailed numerical simulations are used to identify the dominant mechanisms causing mechanical instability. The mechanisms that produce surface instability are found to be fundamentally different from both acoustic parametric instability and shock-driven Richtmyer-Meshkov instability. The time scale of observed and simulated eruption formation is much larger than that of a single shock reflection, in stark contrast to previously studied shock-driven instabilities. Moreover, surface undulations are only found along external, as opposed to internal, soft solid boundaries. Specifically, classic bubble surface instability mechanisms do not occur in our experiments and here we comment only on the new surface undulations found along the outer boundary of solid hydrogel cylinders. Our findings indicate a new class of impulsively excited surface instability that is driven by cycles of internal shock reflections.

Isotopologue ratios are anticipated to be one of the most promising signs of life that can be observed remotely. On Earth, carbon isotopes have been used for decades as evidence of modern and early metabolic processes. In fact, carbon isotopes may be the oldest evidence for life on Earth, though there are alternative geological processes that can lead to the same magnitude of fractionation. However, using isotopologues as biosignature gases in exoplanet atmospheres presents several challenges. Most significantly, we will only have limited knowledge of the underlying abiotic carbon reservoir of an exoplanet. Atmospheric carbon isotope ratios will thus have to be compared against the local interstellar medium or, better yet, their host star. A further substantial complication is the limited precision of remote atmospheric measurements using spectroscopy. The various metabolic processes that cause isotope fractionation cause less fractionation than anticipated measurement precision (biological fractionation is typically 2 to 7%). While this level of precision is easily reachable in the laboratory or with special in situ instruments, it is out of reach of current telescope technology to measure isotope ratios for terrestrial exoplanet atmospheres. Thus, gas isotopologues are poor biosignatures for exoplanets given our current and foreseeable technological limitations.

The decline of natural pollinators necessitates the development of novel pollination technologies. In this work, we propose a drone-enabled autonomous pollination system (APS) that consists of five primary modules: environment sensing, flower perception, path planning, flight control, and pollination mechanisms. These modules are highly dependent upon each other, with each module relying on inputs from the other modules. In this paper, we focus on approaches to the flower perception, path planning, and flight control modules. First, we briefly introduce a flower perception method from our previous work to create a map of flower locations. With a map of flowers, APS path planning is defined as a variant of the Travelling Salesman Problem (TSP). Two path planning approaches are compared based on mixed-integer programming (MIP) and genetic algorithms (GA), respectively. The GA approach is chosen as the superior approach due to the vast computational savings with negligible loss of optimality. To accurately follow the generated path for pollination, we develop a convex optimization approach to the quadrotor flight control problem (QFCP). This approach solves two convex problems. The first problem is a convexified three degree-of-freedom QFCP. The solution to this problem is used as an initial guess to the second convex problem, which is a linearized six degree-of-freedom QFCP. It is found that changing the objective of the second convex problem to minimize the deviation from the initial guess provides improved physical feasibility and solutions similar to a general-purpose optimizer. The path planning and flight control approaches are then tested within a model predictive control (MPC) framework where significant computational savings and embedded adjustments to uncertainty are observed. Coupling the two modules together provides a simple demonstration of how the entire APS will operate in practice.

For over half a century, scientists have contemplated the potential existence of life within the clouds of Venus. Unknown chemistry leaves open the possibility that certain regions of the Venusian atmosphere are habitable. In situ atmospheric measurements with a suite of modern instruments can determine whether the cloud decks possess the characteristics needed to support life as we know it. The key habitability factors are cloud particle droplet acidity and cloud-layer water content. We envision an instrument suite to measure not only the acidity and water content of the droplets (and their variability) but additionally to confirm the presence of metals and other non-volatile elements required for life’s metabolism, verify the existence of organic material, and search for biosignature gases as signs of life. We present an astrobiology-focused mission, science goals, and instruments that can be used on both a large atmospheric probe with a parachute lasting about one hour in the cloud layers (40 to 60 km) or a fixed-altitude balloon operating at about 52 km above the surface. The latter relies on four deployable mini probes to measure habitability conditions in the lower cloud region. The mission doubles as a preparation for sample return by determining whether a subset of cloud particles is non-liquid as well as characterizing the heterogeneity of the cloud particles, thereby informing sample collection and storage methods for a return journey to Earth.

Department of Defense (DoD)

Department of Defense (DoD), The Boeing Company

© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. We report the discovery of a planetary system orbiting TOI-763(aka CD-39 7945), a V = 10.2, high proper motion G-type dwarf star that was photometrically monitored by the TESS space mission in Sector 10. We obtain and model the stellar spectrum and find an object slightly smaller than the Sun, and somewhat older, but with a similar metallicity. Two planet candidates were found in the light curve to be transiting the star. Combining TESS transit photometry with HARPS high-precision radial velocity (RV) follow-up measurements confirm the planetary nature of these transit signals. We determine masses, radii, and bulk densities of these two planets. A third planet candidate was discovered serendipitously in the RV data. The inner transiting planet, TOI-763 b, has an orbital period of Pb = 5.6 d, a mass of Mb = 9.8 ± 0.8 M⊕, and a radius of Rb = 2.37 ± 0.10 R⊕. The second transiting planet, TOI-763 c, has an orbital period of Pc = 12.3 d, a mass of Mc = 9.3 ± 1.0 M⊕, and a radius of Rc = 2.87 ± 0.11 R⊕. We find the outermost planet candidate to orbit the star with a period of ∼48 d. If confirmed as a planet, it would have a minimum mass of Md = 9.5 ± 1.6 M⊕. We investigated the TESS light curve in order to search for a mono transit by planet d without success. We discuss the importance and implications of this planetary system in terms of the geometrical arrangements of planets orbiting G-type stars.

© 2020. The American Astronomical Society. All rights reserved. Although the Transiting Exoplanet Survey Satellite (TESS) primary mission observed the northern and southern ecliptic hemispheres, generally avoiding the ecliptic, and the Kepler space telescope during the K2 mission could only observe near the ecliptic, many of the K2 fields extend far enough from the ecliptic plane that sections overlap with TESS fields. Using photometric observations from both K2 and TESS, combined with archival spectroscopic observations, we globally modeled four known planetary systems discovered by K2 that were observed in the first year of the primary TESS mission. Specifically, we provide updated ephemerides and system parameters for K2-114 b, K2-167 b, K2-237 b, and K2-261 b. These were some of the first K2 planets to be observed by TESS in the first year and include three Jovian sized planets and a sub-Neptune with orbital periods less than 12 days. In each case, the updated ephemeris significantly reduces the uncertainty in prediction of future times of transit, which is valuable for planning observations with the James Webb Space Telescope and other future facilities. The TESS extended mission is expected to observe about half of the K2 fields, providing the opportunity to perform this type of analysis on a larger number of systems.

© ESO 2020. Context. We report the discovery of TOI-519 b (TIC 218795833), a transiting substellar object (R = 1.07 RJup) orbiting a faint M dwarf (V = 17.35) on a 1.26 d orbit. Brown dwarfs and massive planets orbiting M dwarfs on short-period orbits are rare, but more have already been discovered than expected from planet formation models. TOI-519 is a valuable addition to this group of unlikely systems, and it adds towards our understanding of the boundaries of planet formation. Aims. We set out to determine the nature of the Transiting Exoplanet Survey Satellite (TESS) object of interest TOI-519 b. Methods. Our analysis uses a SPOC-pipeline TESS light curve from Sector 7, multicolour transit photometry observed with MuSCAT2 and MuSCAT, and transit photometry observed with the LCOGT telescopes. We estimated the radius of the transiting object using multicolour transit modelling, and we set upper limits for its mass, effective temperature, and Bond albedo using a phase curve model that includes Doppler boosting, ellipsoidal variations, thermal emission, and reflected light components. Results. TOI-519 b is a substellar object with a radius posterior median of 1.07 RJup and 5th and 95th percentiles of 0.66 and 1.20 RJup, respectively, where most of the uncertainty comes from the uncertainty in the stellar radius. The phase curve analysis sets an upper effective temperature limit of 1800 K, an upper Bond albedo limit of 0.49, and a companion mass upper limit of 14 MJup. The companion radius estimate combined with the Teff and mass limits suggests that the companion is more likely a planet than a brown dwarf, but a brown-dwarf scenario is a priori more likely given the lack of known massive planets in ≈ 1 day orbits around M dwarfs with Teff < 3800 K, and given the existence of some (but few) brown dwarfs.

The transit method of exoplanet discovery and characterization has enabled numerous breakthroughs in exoplanetary science. These include measurements of planetary radii, mass–radius relationships, stellar obliquities, bulk density constraints on interior models, and transmission spectroscopy as a means to study planetary atmospheres. The Transiting Exoplanet Survey Satellite (TESS) has added to the exoplanet inventory by observing a significant fraction of the celestial sphere, including many stars already known to host exoplanets. Here we describe the science extraction from TESS observations of known exoplanet hosts during the primary mission. These include transit detection of known exoplanets, discovery of additional exoplanets, detection of phase signatures and secondary eclipses, transit ephemeris refinement, and asteroseismology as a means to improve stellar and planetary parameters. We provide the statistics of TESS known host observations during Cycle 1 and 2, and present several examples of TESS photometry for known host stars observed with a long baseline. We outline the major discoveries from observations of known hosts during the primary mission. Finally, we describe the case for further observations of known exoplanet hosts during the TESS extended mission and the expected science yield.