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Nicolas Payot

Master's Research - Université de Montréal
Supervisor

Publications

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Tri Nguyen
J. Rose
Chris Lovell
Francisco Villaescusa-navarro
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. … (see more)However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Tri Nguyen
J. Rose
Chris Lovell
Francisco Villaescusa-navarro
Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem
Mario Pasquato
Alessandro A. Trani
Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to pred… (see more)ict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active Learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem's decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology.
Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies