Time-scales of close-in exoplanet radio emission variability

We investigate the variability of exoplanetary radio emission using stellar magnetic maps and 3D field extrapolation techniques. We use a sample of hot Jupiter hosting stars, focusing on the HD 179949, HD 189733 and tau Boo systems. Our results indicate two time-scales over which radio emission variability may occur at magnetised hot Jupiters. The first is the synodic period of the star-planet system. The origin of variability on this time-scale is the relative motion between the planet and the interplanetary plasma that is co-rotating with the host star. The second time-scale is the length of the magnetic cycle. Variability on this time-scale is caused by evolution of the stellar field. At these systems, the magnitude of planetary radio emission is anticorrelated with the angular separation between the subplanetary point and the nearest magnetic pole. For the special case of tau Boo b, whose orbital period is tidally locked to the rotation period of its host star, variability only occurs on the time-scale of the magnetic cycle. The lack of radio variability on the synodic period at tau Boo b is not predicted by previous radio emission models, which do not account for the co-rotation of the interplanetary plasma at small distances from the star.Comment: 10 pages, 7 figures, 2 tables, accepted in MNRA

On the environment surrounding close-in exoplanets

Exoplanets in extremely close-in orbits are immersed in a local interplanetary medium (i.e., the stellar wind) much denser than the local conditions encountered around the solar system planets. The environment surrounding these exoplanets also differs in terms of dynamics (slower stellar winds, but higher Keplerian velocities) and ambient magnetic fields (likely higher for host stars more active than the Sun). Here, we quantitatively investigate the nature of the interplanetary media surrounding the hot Jupiters HD46375b, HD73256b, HD102195b, HD130322b, HD179949b. We simulate the three-dimensional winds of their host stars, in which we directly incorporate their observed surface magnetic fields. With that, we derive mass-loss rates (1.9 to 8.0 $\times 10^{-13} M_{\odot}$/yr) and the wind properties at the position of the hot-Jupiters' orbits (temperature, velocity, magnetic field intensity and pressure). We show that these exoplanets' orbits are super-magnetosonic, indicating that bow shocks are formed surrounding these planets. Assuming planetary magnetic fields similar to Jupiter's, we estimate planetary magnetospheric sizes of 4.1 to 5.6 planetary radii. We also derive the exoplanetary radio emission released in the dissipation of the stellar wind energy. We find radio fluxes ranging from 0.02 to 0.13 mJy, which are challenging to be observed with present-day technology, but could be detectable with future higher sensitivity arrays (e.g., SKA). Radio emission from systems having closer hot-Jupiters, such as from tau Boo b or HD189733b, or from nearby planetary systems orbiting young stars, are likely to have higher radio fluxes, presenting better prospects for detecting exoplanetary radio emission.Comment: 15 pages, 5 figures, accepted to MNRA

Exoplanet Transit Variability: Bow Shocks and Winds Around HD 189733b

By analogy with the solar system, it is believed that stellar winds will form bow shocks around exoplanets. For hot Jupiters the bow shock will not form directly between the planet and the star, causing an asymmetric distribution of mass around the exoplanet and hence an asymmetric transit. As the planet orbits thorough varying wind conditions, the strength and geometry of its bow shock will change, thus producing transits of varying shape. We model this process using magnetic maps of HD 189733 taken one year apart, coupled with a 3D stellar wind model, to determine the local stellar wind conditions throughout the orbital path of the planet. We predict the time-varying geometry and density of the bow shock that forms around the magnetosphere of the planet and simulate transit light curves. Depending on the nature of the stellar magnetic field, and hence its wind, we find that both the transit duration and ingress time can vary when compared to optical light curves. We conclude that consecutive near-UV transit light curves may vary significantly and can therefore provide an insight into the structure and evolution of the stellar wind.Comment: 9 Pages, 7 figures. Accepted for publication in Monthly Notices of The Royal Astronomical Societ

Stakeholder theory and management: Understanding longitudinal collaboration networks

This paper explores the evolution of research collaboration networks in the 'stakeholder theory and management' (STM) discipline and identifies the longitudinal effect of co-authorship networks on research performance, i.e., research productivity and citation counts. Research articles totaling 6,127 records from 1989 to 2020 were harvested from the Web of Science Database and transformed into bibliometric data using Bibexcel, followed by applying social network analysis to compare and analyze scientific collaboration networks at the author, institution and country levels. This work maps the structure of these networks across three consecutive sub-periods (t1: 1989-1999; t2: 2000-2010; t3: 2011-2020) and explores the association between authors' social network properties and their research performance. The results show that authors collaboration network was fragmented all through the periods, however, with an increase in the number and size of cliques. Similar results were observed in the institutional collaboration network but with less fragmentation between institutions reflected by the increase in network density as time passed. The international collaboration had evolved from an uncondensed, fragmented and highly centralized network, to a highly dense and less fragmented network in t3. Moreover, a positive association was reported between authors' research performance and centrality and structural hole measures in t3 as opposed to ego-density, constraint and tie strength in t1. The findings can be used by policy makers to improve collaboration and develop research programs that can enhance several scientific fields. Central authors identified in the networks are better positioned to receive government funding, maximize research outputs and improve research community reputation. Viewed from a network's perspective, scientists can understand how collaborative relationships influence research performance and consider where to invest their decision and choices

Learning Deep Robotic Skills on Riemannian manifolds

In this paper, we propose RiemannianFlow, a deep generative model that allows robots to learn complex and stable skills evolving on Riemannian manifolds. Examples of Riemannian data in robotics include stiffness (symmetric and positive definite matrix (SPD)) and orientation (unit quaternion (UQ)) trajectories. For Riemannian data, unlike Euclidean ones, different dimensions are interconnected by geometric constraints which have to be properly considered during the learning process. Using distance preserving mappings, our approach transfers the data between their original manifold and the tangent space, realizing the removing and re-fulfilling of the geometric constraints. This allows to extend existing frameworks to learn stable skills from Riemannian data while guaranteeing the stability of the learning results. The ability of RiemannianFlow to learn various data patterns and the stability of the learned models are experimentally shown on a dataset of manifold motions. Further, we analyze from different perspectives the robustness of the model with different hyperparameter combinations. It turns out that the model's stability is not affected by different hyperparameters, a proper combination of the hyperparameters leads to a significant improvement (up to 27.6%) of the model accuracy. Last, we show the effectiveness of RiemannianFlow in a real peg-in-hole (PiH) task where we need to generate stable and consistent position and orientation trajectories for the robot starting from different initial poses

Learning Stable Robotic Skills on Riemannian Manifolds

In this paper, we propose an approach to learn stable dynamical systems evolving on Riemannian manifolds. The approach leverages a data-efficient procedure to learn a diffeomorphic transformation that maps simple stable dynamical systems onto complex robotic skills. By exploiting mathematical tools from differential geometry, the method ensures that the learned skills fulfill the geometric constraints imposed by the underlying manifolds, such as unit quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices for impedance, while preserving the convergence to a given target. The proposed approach is firstly tested in simulation on a public benchmark, obtained by projecting Cartesian data into UQ and SPD manifolds, and compared with existing approaches. Apart from evaluating the approach on a public benchmark, several experiments were performed on a real robot performing bottle stacking in different conditions and a drilling task in cooperation with a human operator. The evaluation shows promising results in terms of learning accuracy and task adaptation capabilities.Comment: 16 pages, 10 figures, journa

Trajectory Optimization on Matrix Lie Groups with Differential Dynamic Programming and Nonlinear Constraints

Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and the optimization of control policies on these manifolds is a fundamental problem. In this work, we propose a novel approach for trajectory optimization on matrix Lie groups using an augmented Lagrangian-based constrained discrete Differential Dynamic Programming. The method involves lifting the optimization problem to the Lie algebra in the backward pass and retracting back to the manifold in the forward pass. In contrast to previous approaches which only addressed constraint handling for specific classes of matrix Lie groups, the proposed method provides a general approach for nonlinear constraint handling for generic matrix Lie groups. We also demonstrate the effectiveness of the method in handling external disturbances through its application as a Lie-algebraic feedback control policy on SE(3). Experiments show that the approach is able to effectively handle configuration, velocity and input constraints and maintain stability in the presence of external disturbances.Comment: 10 pages, 7 figure

Low latency via redundancy

Low latency is critical for interactive networked applications. But while we know how to scale systems to increase capacity, reducing latency --- especially the tail of the latency distribution --- can be much more difficult. In this paper, we argue that the use of redundancy is an effective way to convert extra capacity into reduced latency. By initiating redundant operations across diverse resources and using the first result which completes, redundancy improves a system's latency even under exceptional conditions. We study the tradeoff with added system utilization, characterizing the situations in which replicating all tasks reduces mean latency. We then demonstrate empirically that replicating all operations can result in significant mean and tail latency reduction in real-world systems including DNS queries, database servers, and packet forwarding within networks

Magnetic field, differential rotation and activity of the hot-Jupiter hosting star HD 179949

HD 179949 is an F8V star, orbited by a giant planet at ~8 R* every 3.092514 days. The system was reported to undergo episodes of stellar activity enhancement modulated by the orbital period, interpreted as caused by Star-Planet Interactions (SPIs). One possible cause of SPIs is the large-scale magnetic field of the host star in which the close-in giant planet orbits. In this paper we present spectropolarimetric observations of HD 179949 during two observing campaigns (2009 September and 2007 June). We detect a weak large-scale magnetic field of a few Gauss at the surface of the star. The field configuration is mainly poloidal at both observing epochs. The star is found to rotate differentially, with a surface rotation shear of dOmega=0.216\pm0.061 rad/d, corresponding to equatorial and polar rotation periods of 7.62\pm0.07 and 10.3\pm0.8 d respectively. The coronal field estimated by extrapolating the surface maps resembles a dipole tilted at ~70 degrees. We also find that the chromospheric activity of HD 179949 is mainly modulated by the rotation of the star, with two clear maxima per rotation period as expected from a highly tilted magnetosphere. In September 2009, we find that the activity of HD 179949 shows hints of low amplitude fluctuations with a period close to the beat period of the system.Comment: Accepted for publication in Monthly Notices of The Royal Astronomical Societ