arXiv subjects

Ahmad Farhani Asl

Publications and source records attributed to Ahmad Farhani Asl.

3 recordsLinked to original sources

arXiv2026

Predicting the final states of binary-single scattering with machine learning

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. However, predicting their final outcomes remains an open question due to the intrinsic chaotic nature of the three-body problem. This challenge motivates the adoption of data-driven machine learning (ML) methods. Aims. We investigate whether ML can predict the final outcomes of binary-single encounters from initial conditions alone. Methods. We generated 5.8 million binary-single scattering simulations using the REBOUND N-body package with the IAS15 integrator. A cascaded binary classification strategy, comprising four sequential XGBoost classifiers, and a single multi-class model were trained on the synthetic dataset and compared. Results. The cascaded strategy outperforms the single multi-class model across all metrics. F1-scores for the cascaded models exceed 0.92, with precision-recall area under the curve (PR-AUC) values reaching 0.99, compared to 0.95 for the multi-class model. Feature importance analysis identifies encounter timescale, binary hardness, and mass ratio as key predictors. Misclassification analysis shows that prediction failures concentrate near chaotic boundaries where the outcome is sensitive to small perturbations. Speed benchmarks demonstrate that the cascaded models are up to 300 times faster than direct N-body integrations. However, all models fail to generalize to new datasets, highlighting a key limitation. Conclusions. This study demonstrates that, for any specific environment and data distribution, the proposed cascaded ML strategy provides a robust and rapid framework for predicting binary-single scattering outcomes.

astro-ph.GA↗

arXiv2026

Machine learning prediction of binary formation in three-body gravitational encounters

Three-body encounters are frequent events in stellar systems, intrinsically chaotic, and computationally costly to model with direct N-body integration. Predicting whether such encounters lead to binary formation is therefore challenging, particularly in large-scale simulations. We aim to develop an accurate and physically interpretable machine-learning model that predicts binary formation from the initial conditions of three-body encounters, to assess its reliability, and to identify the physical parameters that most strongly determine the outcome. We trained an XGBoost binary classifier on a balanced dataset of three-body scattering experiments computed with the REBOUND code and the IAS15 integrator. The input to the model consists of 30 physically motivated features describing the masses, energies, and kinematics of the initial configuration. The classifier achieves excellent performance on a balanced test set, with accuracy, precision, recall, and F1-score all above 0.94, and with ROC-AUC and PR-AUC values of 0.99. Feature-importance analysis shows that the outcome is governed primarily by the mass hierarchy and hardness ratio of the encounter, followed by velocity fraction and mass entropy. The predicted probabilities are well calibrated, with an expected calibration error (ECE) of 0.02. Inference is approximately 400 times faster than direct N-body integration. The model also generalizes well across encounter radii, although its performance decreases in the weak-interaction regime where binary formation becomes intrinsically rare. These results show that machine learning can provide fast, accurate, and physically interpretable predictions of binary formation in three-body encounters. Such models offer a practical complement to direct N-body simulations and may enable efficient probability estimates in large-scale simulations of stellar systems.

astro-ph.GA↗

arXiv2022

The Escape of Globular Clusters from the Satellite Dwarf Galaxies of the Milky Way

Using numerical simulations, we have studied the escape of globular clusters (GCs) from the satellite dwarf spheroidal galaxies (dSphs) of the Milky Way (MW). We start by following the orbits of a large sample of GCs around dSphs in the presence of the MW potential field. We then obtain the fraction of GCs leaving their host dSphs within a Hubble Time. We model dSphs by a Hernquist density profile with masses between $10^7\,\mathrm{M}_{\odot}$ and $7\times 10^9\,\mathrm{M}_{\odot}$. All dSphs lie on the Galactic disc plane, but they have different orbital eccentricities and apogalactic distances. We compute the escape fraction of GCs from 13 of the most massive dSphs of the MW, using their realistic orbits around the MW (as determined by Gaia). The escape fraction of GCs from 13 dSphs is in the range $12\%$ to $93\%$. The average escape time of GCs from these dSphs was less than 8 $\,\mathrm{Gyrs}$, indicating that the escape process of GCs from dSphs was over. We then adopt a set of observationally-constrained density profiles for specific case of the Fornax dSph. According to our results, the escape fraction of GCs shows a negative correlation with both the mass and the apogalactic distance of the dSphs, as well as a positive correlation with the orbital eccentricity of dSphs. In particular, we find that the escape fraction of GCs from the Fornax dSph is between $13\%$ and $38\%$. Finally, we observe that when GCs leave their host dSphs, their final orbit around the MW does not differ much from their host dSphs.

astro-ph.GA↗