1 code implementation • 27 Sep 2017 • Yao-Yuan Mao, Eve Kovacs, Katrin Heitmann, Thomas D. Uram, Andrew J. Benson, Duncan Campbell, Sofía A. Cora, Joseph DeRose, Tiziana Di Matteo, Salman Habib, Andrew P. Hearin, J. Bryce Kalmbach, K. Simon Krughoff, François Lanusse, Zarija Lukić, Rachel Mandelbaum, Jeffrey A. Newman, Nelson Padilla, Enrique Paillas, Adrian Pope, Paul M. Ricker, Andrés N. Ruiz, Ananth Tenneti, Cristian Vega-Martínez, Risa H. Wechsler, Rongpu Zhou, Ying Zu, for the LSST Dark Energy Science Collaboration
The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
2 code implementations • 1 Aug 2019 • Niall Jeffrey, François Lanusse, Ofer Lahav, Jean-Luc Starck
With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent.
Cosmology and Nongalactic Astrophysics
1 code implementation • 22 Oct 2019 • Vanessa Böhm, François Lanusse, Uroš Seljak
We develop a generative model-based approach to Bayesian inverse problems, such as image reconstruction from noisy and incomplete images.
no code implementations • 2 Jun 2020 • Tom Charnock, Laurence Perreault-Levasseur, François Lanusse
In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models.
1 code implementation • 12 Jul 2022 • Denise Lanzieri, François Lanusse, Jean-Luc Starck
We present a new scheme to compensate for the small-scales approximations resulting from Particle-Mesh (PM) schemes for cosmological N-body simulations.
no code implementations • 12 Jul 2022 • Justine Zeghal, François Lanusse, Alexandre Boucaud, Benjamin Remy, Eric Aubourg
Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions.