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CatBoost versus Spectral Energy Distribution-Fitting: Estimating Galaxy Properties under Controlled Photometric Incompleteness
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
Estimating galaxy physical parameters from photometric data is fundamentally challenged by missing measurements that are endemic to astronomical surveys. Using a mock catalog from…
Machine learning prediction of binary formation in three-body gravitational encounters
Ahmad Farhani Asl
Three-body encounters are frequent events in stellar systems, intrinsically chaotic, and computationally costly to model with direct N-body integration. Predicting whether such enc…
Predicting the final states of binary-single scattering with machine learning
Ahmad Farhani Asl, David Fonseca Mota, Dennis Fremstad +1
Context. Binary-single encounters are particularly frequent in dense stellar environments, where they play a central role in shaping the dynamical evolution of their host systems.…
COSMOS2025: A Machine Learning Census of Massive Quiescent Galaxies at
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
The paper introduces a machine‑learning classifier (CatBoost) trained on mock photometry from semi‑analytic models to identify massive quiescent galaxies at redshifts 2.5–5 in the…
Baryonic mass budgets in the central regions of the Bullet Cluster and their consistency with strong lensing in MOND
Dong Zhang, Hosein Haghi, Elena Asencio +8
Strong lensing observations of the Bullet Cluster have traditionally been regarded as strong evidence for dark matter and a major challenge to Milgromian dynamics (MOND). The offse…