96 citations · 328 across the 30 of their papers we have counts for
6 papers · 1 filter
1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments
Marco Canducci, Petra Awad, Abolfazl Taghribi +7
Filaments are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied…
Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure
Petra Awad, Reynier Peletier, Marco Canducci +6
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the C…
Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics
Giuseppe Longo, Erzsébet Merényi, Peter Tino
Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large…
Model-Coupled Autoencoder for Time Series Visualisation
Nikolaos Gianniotis, Sven D. Kügler, Peter Tiňo +1
We present an approach for the visualisation of a set of time series that combines an echo state network with an autoencoder. For each time series in the dataset we train an echo s…
Kernel regression estimates of time delays between gravitationally lensed fluxes
Sultanah AL Otaibi, Peter Tiňo, Juan C Cuevas-Tello +2
Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have b…
Autoencoding Time Series for Visualisation
Nikolaos Gianniotis, Dennis Kügler, Peter Tino +2
We present an algorithm for the visualisation of time series. To that end we employ echo state networks to convert time series into a suitable vector representation which is capabl…