22 citations · 23 across the 5 of their papers we have counts for
5 papers
Nonparametric signal separation in very-high-energy gamma ray observations with probabilistic neural networks
Marion Ullmo, Emmanuel Moulin
An intriguing challenge in observational astronomy is the separation signals in areas where multiple signals intersect. A typical instance of this in very-high-energy (VHE, E$\gtrs…
Predicting large scale cosmological structure evolution with generative adversarial network-based autoencoders
Marion Ullmo, Nabila Aghanim, Aurélien Decelle +1
Predicting the nonlinear evolution of cosmic structure from initial conditions is typically approached using Lagrangian, particle-based methods. These techniques excel in terms of…
Joint H.E.S.S. and Fermi-LAT analysis of the region around PSR J1813-1749
T. Wach, A. M. W. Mitchell, V. Joshi +1
HESS J1813-178 is one of the brightest sources detected during the first HESS Galactic Plane survey. The compact source, also detected by MAGIC, is believed to be a pulsar wind neb…
Modelling of highly extended Gamma-ray emission around the Geminga Pulsar as detected with H.E.S.S
A. M. W. Mitchell, S. Caroff
Geminga is an enigmatic radio-quiet gamma-ray pulsar located at a mere 250 pc distance from Earth. Extended very-high-energy gamma-ray emission around the pulsar has been detected…
Encoding large scale cosmological structure with Generative Adversarial Networks
Marion Ullmo, Aurélien Decelle, Nabila Aghanim
Recently a type of neural networks called Generative Adversarial Networks (GANs) has been proposed as a solution for fast generation of simulation-like datasets, in an attempt to b…