Search

The encounter between the Jovian co-rotating plasma and Ganymede gives rise to electromagnetic waves that propagate along the magnetic field lines and accelerate particles by resonant or non-resonant wave-particle interaction. They ultimately precipitate into Jupiter's atmosphere and trigger auroral emissions. In this study, we use Juno/JADE, Juno/UVS data, and magnetic field line tracing to characterize the properties of electrons accelerated by the Ganymede-magnetosphere interaction in the far-field region. We show that the precipitating energy flux exhibits an exponential decay as a function of downtail distance from the moon, with an e-folding value of 29{\deg}, consistent with previous UV observations from the Hubble Space Telescope (HST). We characterize the electron energy distributions and show that two distributions exist. Electrons creating the Main Alfv\'en Wing (MAW) spot and the auroral tail always have broadband distribution and a mean characteristic energy of 2.2 keV while in the region connected to the Transhemispheric Electron Beam (TEB) spot the electrons are distributed non-monotonically, with a higher characteristic energy above 10 keV. Based on the observation of bidirectional electron beams, we suggest that Juno was located within the acceleration region during the 11 observations reported. We thus estimate that the acceleration region is extended, at least, between an altitude of 0.5 and 1.3 Jupiter radius above the 1-bar surface. Finally, we estimate the size of the interaction region in the Ganymede orbital plane using far-field measurements. These observations provide important insights for the study of particle acceleration processes involved in moon-magnetosphere interactions., Comment: Accepted for publication in JGR: Space Physics

At Jupiter, part of the auroral radio emissions are induced by the Galilean moons Io, Europa and Ganymede. Until now, except for Ganymede, they have been only remotely detected, using ground-based radio-telescopes or electric antennas aboard spacecraft. The polar trajectory of the Juno orbiter allows the spacecraft to cross the range of magnetic flux tubes which sustain the various Jupiter-satellite interactions, and in turn to sample in situ the associated radio emission regions. In this study, we focus on the detection and the characterization of radio sources associated with Io, Europa and Ganymede. Using electric wave measurements or radio observations (Juno/Waves), in situ electron measurements (Juno/JADE-E), and magnetic field measurements (Juno/MAG) we demonstrate that the Cyclotron Maser Instability (CMI) driven by a loss-cone electron distribution function is responsible for the encountered radio sources. We confirmed that radio emissions are associated with Main (MAW) or Reflected Alfv\'en Wing (RAW), but also show that for Europa and Ganymede, induced radio emissions are associated with Transhemispheric Electron Beam (TEB). For each traversed radio source, we determine the latitudinal extension, the CMI-resonant electron energy, and the bandwidth of the emission. We show that the presence of Alfv\'en perturbations and downward field aligned currents are necessary for the radio emissions to be amplified.

Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations. Recent efforts to bridge the gap between GPs and deep NNs have yielded a new class of inter-domain variational GPs in which the inducing variables correspond to hidden units of a feedforward NN. In this work, we examine some practical issues associated with this approach and propose an extension that leverages the orthogonal decomposition of GPs to mitigate these limitations. In particular, we introduce spherical inter-domain features to construct more flexible data-dependent basis functions for both the principal and orthogonal components of the GP approximation and show that incorporating NN activation features under this framework not only alleviates these shortcomings but is more scalable than alternative strategies. Experiments on multiple benchmark datasets demonstrate the effectiveness of our approach., Comment: To appear in the Proceedings of the 40th International Conference on Machine Learning (ICML2023)

During its 53-day polar orbit around Jupiter, Juno often crosses the boundaries of the Jovian magnetosphere (namely the magnetopause and bow shock). From the boundary locations, the upstream solar wind dynamic pressure can be inferred, which in turn illustrates the state of compression or relaxation of the system. The aim of this study is to examine Jovian radio emissions during magnetospheric compressions, in order to determine the relationship between the solar wind and Jovian radio emissions. In this paper, we give a complete list of bow shock and magnetopause crossings (from June 2016 to August 2022), along with some extra informations (e.g. solar wind dynamic pressure and position of the standoff distances inferred from Joy et al. (2002)). We then select two compression events that occur in succession (inferred from magnetopause crossings) and we present a case study of the response of the Jovian radio emissions. We demonstrate that magnetospheric compressions lead to the activation of new radio sources. Newly activated broadband kilometric emissions are observed almost simultaneously to compression of the magnetosphere, with sources covering a large range of longitudes. Decametric emission sources are seen to be activated more than one rotation later only at specific longitudes and dusk local times. Finally, the activation of narrowband kilometric radiation is not observed during the compression phase, but when the magnetosphere is in its expansion phase.

In a system of processes that communicate asynchronously by means of FIFO channels, there are many options in which these channels can be laid out. In this paper, we compare channel layouts in how they affect the behaviour of the system using an ordering based on splitting and merging channels. This order induces a simulation relation, from which the preservation of safety properties follows. Also, we identify conditions under which the properties reachability, deadlock freedom and confluence are preserved when changing the channel layout.

The SPectrogram Analysis and Cataloguing Environment (SPACE) tool is an interactive python tool designed to label radio emission features of interest in a time-frequency map (called 'dynamic spectrum'). The program uses Matplotlib's Polygon Selector widget to allow a user to select and edit an undefined number of vertices on top of the dynamic spectrum before closing the shape (polygon). Multiple polygons may be drawn on any spectrum, and the feature name along with the coordinates for each polygon vertex are saved into a '.json' file as per the 'Time-Frequency Catalogue' (TFCat) format along with other data such as the feature id, observer name, and data units. This paper describes the first official stable release (version 2.0) of the tool., Comment: 9 pages, 1 Figure

We investigate the beaming of 11 Io-Jupiter decametric (Io-DAM) emissions observed by Juno/Waves, the Nan\c cay Decameter Array and NenuFAR. Using an up-to-date magnetic field model and three methods to position the active Io Flux Tube (IFT), we accurately locate the radiosources and determine their emission angle $\theta$ from the local magnetic field vector. These methods use (i) updated models of the IFT equatorial lead angle, (ii) ultraviolet (UV) images of Jupiter's aurorae and (iii) multi-point radio measurements. The kinetic energy $E_{e-}$ of source electrons is then inferred from $\theta$ in the framework of the Cyclotron Maser Instability. The precise position of the active IFT achieved from methods (ii,iii) can be used to test the effective torus plasma density. Simultaneous radio/UV observations reveal that multiple Io-DAM arcs are associated with multiple UV spots and provide the first direct evidence of an Io-DAM arc associated with a trans-hemispheric beam UV spot. Multi-point radio observations probe the Io-DAM sources at various altitudes, times and hemispheres. Overall, $\theta$ varies a function of frequency (altitude), by decreasing from $75^\circ-80^\circ$ to $70^\circ-75^\circ$ over $10-40$ MHz with slightly larger values in the northern hemisphere, and independently varies as a function of time (or longitude of Io). Its uncertainty of a few degrees is dominated by the error on the longitude of the active IFT. The inferred values of $E_{e-}$ also vary as a function of altitude and time. For the 11 investigated cases, they range from 3 to 16 keV, with a $6.6\pm2.7$ keV average.

To control African swine fever (ASF) efficiently, easily interpretable metrics of the outbreak dynamics are needed to plan and adapt the required measures. We found that the spread pattern of African Swine Fever cases in wild boar follows the mechanics of a diffusion process, at least in the early phase, for the cases that occurred in Germany. Following incursion into a previously unaffected area, infection disseminates locally within a naive and abundant wild boar population. Using real case data for Germany, we derive statistics about the time differences and distances between consecutive case reports. With the use of these statistics, we generate an ensemble of random walkers (continuous time random walks, CTRW) that resemble the properties of the observed outbreak pattern as one possible realization of all possible disease dissemination patterns. The trained random walker ensemble yields the diffusion constant, the affected area, and the outbreak velocity of early ASF spread in wild boar. These quantities are easy to interpret, robust, and may be generalized or adapted to different regions. Therefore, diffusion metrics can be useful descriptors of early disease dynamics and help facilitate efficient control of African Swine Fever.

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun., Comment: 12 pages, 10 figures, accepted for publication in Astronomical instrumentation section of Astronomy & Astrophysics 24/08/2021

Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI) function. The need to ensure analytical tractability of the predictive often poses limitations that can hinder the efficiency and applicability of BO. In this paper, we cast the computation of EI as a binary classification problem, building on the link between class-probability estimation and density-ratio estimation, and the lesser-known link between density-ratios and EI. By circumventing the tractability constraints, this reformulation provides numerous advantages, not least in terms of expressiveness, versatility, and scalability., Comment: preprint, under review

Occultations of the Jovian low frequency radio emissions by the Galilean moons have been observed by the PWS instrument of the Galileo spacecraft. We show that the ExPRES (Exoplanetary and Planetary Radio Emission Simulator) code accurately models the temporal occurrence of the occultations in the whole spectral range observed by Galileo/PWS. This validates of the ExPRES code. The method can be applied for preparing the JUICE moon flyby science operation planning. Occultations of the Jovian low frequency radio emissions by the Galilean moons have been observed by the PWS (Plasma Wave Science) instrument of the Galileo spacecraft. We show that the ExPRES (Exoplanetary and Planetary Radio Emission Simulator) code accurately models the temporal occurrence of the occultations in the whole spectral range observed by Galileo/PWS. This validates of the ExPRES code. In addition to supporting the analysis of the science observations, the method can be applied for preparing the JUICE moon flyby science operation planning., Comment: Accepted for publication in Planet. Space Sci. Supplementary material available at: https://doi.org/10.25935/8ZFF-NX36

We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization. At the heart of our method is a probabilistic model that can simultaneously reason across hyperparameters and resource levels, and supports decision-making in the presence of pending evaluations. We demonstrate the effectiveness of our method on a wide range of challenging benchmarks, for tabular data, image classification and language modelling, and report substantial speed-ups over current state-of-the-art methods. Our new methods, along with asynchronous baselines, are implemented in a distributed framework which will be open sourced along with this publication.