Jean‐Paul Kneib

Florian Cabot, Yves Revaz, Jean-Paul Kneib et al.

VIRUP is a new C++ open source software that provides an interactive virtual reality environment to navigate through large scientific astrophysical datasets obtained from both observations and simulations. It is tailored to visualize terabytes of data, rendering at 90 frames per second in order to ensure an optimal immersion experience. While VIRUP has initially been designed to work with gaming virtual reality headsets, it supports different modern immersive systems like 3D screens, 180 deg. domes or 360 deg. panorama. VIRUP is scriptable thanks to the Python language, a feature that allows to immerse visitors through pre-selected scenes or to pre-render sequences to create movies. A companion video (https://www.youtube.com/watch?v=KJJXbcf8kxA) to the last SDSS 2020 release as well as a 21 minute long documentary, The Archaeology of Light, https://go.epfl.ch/ArchaeologyofLight have been both 100% produced using VIRUP.

A. Tajja, A. Aghabiglou, E. Tolley et al.

Recently, the R2D2 paradigm, standing for ''Residual-to-Residual DNN series for high-Dynamic-range imaging'', was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R2D2 are limited to planar imaging on small fields of view, failing to meet the spherical-imaging requirement of modern telescopes observing wide fields. To address this limitation, we propose the spherical-imaging extension S-R2D2. Firstly, as R2D2, S-R2D2 encapsulates its minor cycles in existing 2D-Euclidean deep neural network (DNN) architectures, but adapts its iterative scheme to incorporate the wide-field measurement model mapping a spherical image to visibility data. We implemented this model as the composition of an efficient Fourier-based interpolator mapping the spherical image onto the equatorial plane, with the standard RI operator mapping the equatorial-plane image to visibility data. Importantly, the interpolation step must inevitably be performed at a lower-than-optimal resolution on the plane, to meet the high-resolution requirement on the sphere of wide-field imaging while preserving scalability. Therefore, secondly, we design S-R2D2's DNN training loss to jointly learn to correct the interpolation approximations and identify residual image structures on the sphere, ensuring consistency with the spherical ground truth using the adjoint plane-to-sphere interpolator. Finally, we demonstrate through simulations S-R2D2's capability to perform fast and accurate reconstructions of spherical monochromatic intensity images, across high-resolution, high-dynamic-range settings.

Matin Macktoobian, Francesco Basciani, Denis Gillet et al.

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination are so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of a coordination before its execution. However, a formal verification method does not exist for this purpose. In this paper, we instead use machine learning to predict the convergence of astrobots swarm. In particular, we propose a weighted $k$-NN-based algorithm which requires the initial status of a swarm as well as its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy.