activity
20242026
collaborators

7 papers

astro-ph.EP2026

Causes of Hot Jupiter Inflation from Causal Discovery

Zehao Jin, Mohamad Ali-Dib, Yujia Zheng +3

Hot Jupiters often have radii larger than predicted by standard cooling--contraction models, but it remains unclear which process supplies or preserves the extra internal heat. We…

astro-ph.GA2026

Causal Reversal in the $M_\unicode{x25CF}\unicode{x2013}σ_0$ Relation: Implications for High-Redshift Supermassive Black Hole Mass Estimates

Benjamin L. Davis, Saakshi More, Zehao Jin +3

The nascent methodology of applying the principles of causal discovery to astrophysical data has produced affirming results about deeply held theories concerning the causal nature…

astro-ph.GA2026

Two faces of Gaia-Sausage-Enceladus: Mining the chemical abundance space with graph attention networks

Milan Quandt-Rodriguez, Sara Lucatello, Lorenzo Spina +2

Recent studies suggest that chemical abundances hold the key to disentangling halo substructure, providing a more reliable tracer than dynamics alone. We aim to probe the Milky Way…

astro-ph.GA2025

GW emission and relativistic dynamical friction in intermediate mass ratio inspirals

P. Di Cintio, G. Bertone, C. Chiari +4

We present a set of preliminary simulations of intermediate mass ratio inspirals (IMRIs) inside dark matter (DM) spikes accounting for post-Newtonian corrections the interaction be…

astro-ph.GA2025

Chaos in violent relaxation dynamics. Disentangling micro- and macro-chaos in numerical experiments of dissipationless collapse

Simone Sartorello, Pierfrancesco Di Cintio, Alessandro Alberto Trani +1

Violent relaxation (VR) is often regarded as the mechanism leading stellar systems to collisionless meta equilibrium via rapid changes in the collective potential. We investigate t…

astro-ph.GA2025

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…