5 citations · 8 across the 6 of their papers we have counts for
6 papers
Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin, Mario Pasquato, Benjamin L. Davis +9
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) w…
Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot, Pablo Lemos, Laurence Perreault-Levasseur +3
Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show ho…
Bayesian Imaging for Radio Interferometry with Score-Based Priors
Noe Dia, M. J. Yantovski-Barth, Alexandre Adam +5
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner. We use a sc…
The search for the lost attractor
Mario Pasquato, Syphax Haddad, Pierfrancesco Di Cintio +8
N-body systems characterized by inverse square attractive forces may display a self similar collapse known as the gravo-thermal catastrophe. In star clusters, collapse is halted by…
Causa prima: cosmology meets causal discovery for the first time
Mario Pasquato, Zehao Jin, Pablo Lemos +2
In astrophysics, experiments are impossible. We thus must rely exclusively on observational data. Other observational sciences increasingly leverage causal inference methods, but t…
Interpretable machine learning for finding intermediate-mass black holes
Mario Pasquato, Piero Trevisan, Abbas Askar +4
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle pred…