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Improved particle spray algorithm for modeling globular cluster streams

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arxiv 2408.01496 v3 pith:JBT3EI2B submitted 2024-08-02 astro-ph.GA

classification astro-ph.GA
keywords algorithmdistributionsparticlesstreamstreamsclusterclustersdevelop
verification ladder T0 review T1 audit T2 compute T3 formal
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Stellar streams that emerge from globular clusters (GCs) are thin stellar structures spread along the orbits of progenitor clusters. Numerical modeling of these streams is essential for understanding their interaction with the host galaxy's mass distribution. Traditional methods are either computationally expensive or oversimplified, motivating us to develop a fast and accurate approach using a particle spray algorithm. By conducting a series of N-body simulations of GCs orbiting a host galaxy, we find that the position and velocity distributions of newly-escaped stream particles are consistent across various GC masses and orbital parameters. Based on these distributions, we develop a new algorithm that avoids computing the detailed internal cluster dynamics by directly drawing tracer particles from these distributions. This algorithm correctly reproduces the action space distribution of stream particles and achieves a 10% accuracy in stream morphology and velocities compared to N-body simulations. To facilitate broader use, we have implemented this algorithm in galactic dynamics codes agama, gala, galax, and galpy.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neighbors, Not Kin: Kinematic evidence for tidal tails from NGC 7492 along the Sagittarius stream

    astro-ph.GA 2026-07 accept novelty 6.0 of 10

    GIRAFFE radial velocities and metallicities confirm NGC 7492 tidal tails extending ≥1.8 deg, kinematically and chemically distinct from the overlapping Sagittarius stream.

  2. Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks

    astro-ph.GA 2025-02 conditional novelty 6.0 of 10

    A graph neural network plus simulation-based inference infers subhalo mass and velocity from simulated GD-1 streams with 3 to 11 times tighter mass constraints than the 1D power spectrum, with better-calibrated posteriors.

  3. Constraints on the population level distribution of nearby Dark Matter halo shapes with extragalactic streams

    astro-ph.GA 2026-07 conditional novelty 5.5 of 10

    A gold subsample of 17 photometry-only extragalactic streams yields a mildly oblate dark-matter halo population with mean flattening μ_q ≈ 0.72 and scatter σ_q ≈ 0.34.

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