1 code implementation • 31 Oct 2023 • Veronica Saz Ulibarrena, Philipp Horn, Simon Portegies Zwart, Elena Sellentin, Barry Koren, Maxwell X. Cai
To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate.
no code implementations • 1 Jul 2020 • Stella Reino, Elena M. Rossi, Robyn E. Sanderson, Elena Sellentin, Amina Helmi, Helmer H. Koppelman, Sanjib Sharma
We fit a common gravitational potential to multiple stellar streams simultaneously by maximizing the clustering of the stream stars in action space.
Astrophysics of Galaxies
no code implementations • 12 Jul 2019 • Andrea Manrique-Yus, Elena Sellentin
We develop a fully non-invasive use of machine learning in order to enable open research on Euclid-sized data sets.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
1 code implementation • 1 Aug 2017 • Vittorio Tansella, Camille Bonvin, Ruth Durrer, Basundhara Ghosh, Elena Sellentin
In particular, we show that gravitational lensing modifies the multipoles of the correlation function and of the power spectrum by a few percent at redshift z=1 and by up to 30% and more at z=2.
Cosmology and Nongalactic Astrophysics
2 code implementations • 11 Apr 2017 • Alan Heavens, Yabebal Fantaye, Arrykrishna Mootoovaloo, Hans Eggers, Zafiirah Hosenie, Steve Kroon, Elena Sellentin
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sampled posterior distributions.
Computation Cosmology and Nongalactic Astrophysics