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Found 5 papers, 3 papers with code
A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks
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.
Galactic potential constraints from clustering in action space of combined stellar stream data
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
Euclid-era cosmology for everyone: Neural net assisted MCMC sampling for the joint 3x2 likelihood
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
The full-sky relativistic correlation function and power spectrum of galaxy number counts: I. Theoretical aspects
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
Marginal Likelihoods from Monte Carlo Markov Chains
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
