21 citations · 35 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis
Zehao Jin, Mario Pasquato, Benjamin L. Davis +2
We present the first steps toward applying causal representation learning to astronomy. Following up on previous work that introduced causal discovery to the field for the first ti…
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…
Variable Star Light Curves in Koopman Space
Nicolas Mekhaël, Mario Pasquato, Gaia Carenini +4
We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variabl…
Quantitatively rating galaxy simulations against real observations with anomaly detection
Zehao Jin, Andrea V. Macciò, Nicholas Faucher +6
Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realis…
Sharpening the dark matter signature in gravitational waveforms II: Numerical simulations with the NbodyIMRI code
Bradley J. Kavanagh, Theophanes K. Karydas, Gianfranco Bertone +2
Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a d…
Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem
Nicolas Payot, Mario Pasquato, Alessandro Alberto Trani +2
Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g.…
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…
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…