KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering

Abstract

We present KineticXGPU, a PyTorch-based implementation of the 2→2 elastic self-collision operator for dark-sector momentum distributions. The discretized collision operator can be expressed as tensor contractions and is therefore well suited for GPUs. As an application, we study a two-source freeze-in scenario in which the final distribution can develop a bimodal shape. We show that increasing the strength of elastic self-interactions progressively erases this structure and drives the distribution toward a Maxwell--Boltzmann distribution. We find that 2→2 self-interactions can reduce the warmness of nonthermal frozen-in dark matter, illustrating how they may alleviate structure-formation constraints in late-decay scenarios. We compare the phase-space formulation with a set of fluid equations that couple the number density and velocity dispersion. We also compare CPU and GPU runtimes and demonstrate the computational advantage of the tensorized approach. The code is publicly available on GitHub.

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BibTeX RIS

BibTeX

@misc{cervantes2026kineticxgpu,
  title = {{KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering}},
  author = {Esau Cervantes},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2607.00755},
  eprint = {2607.00755},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.00755},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering
AU  - Esau Cervantes
PY  - 2026
DA  - 2026/10/02
PB  - arXiv
DO  - 10.48550/arxiv.2607.00755
UR  - https://arxiv.org/abs/2607.00755
AB  - We present KineticXGPU, a PyTorch-based implementation of the 2→2 elastic self-collision operator for dark-sector momentum distributions. The discretized collision operator can be expressed as tensor contractions and is therefore well suited for GPUs. As an application, we study a two-source freeze-in scenario in which the final distribution can develop a bimodal shape. We show that increasing the strength of elastic self-interactions progressively erases this structure and drives the distribution toward a Maxwell--Boltzmann distribution. We find that 2→2 self-interactions can reduce the warmness of nonthermal frozen-in dark matter, illustrating how they may alleviate structure-formation constraints in late-decay scenarios. We compare the phase-space formulation with a set of fluid equations that couple the number density and velocity dispersion. We also compare CPU and GPU runtimes and demonstrate the computational advantage of the tensorized approach. The code is publicly available on GitHub.
ER  -

Abstract

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