Presents a tensorized GPU implementation of the 2-to-2 elastic self-collision operator for dark-sector particles and applies it to a two-source freeze-in scenario where self-interactions erase bimodal features.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.
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KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering
Presents a tensorized GPU implementation of the 2-to-2 elastic self-collision operator for dark-sector particles and applies it to a two-source freeze-in scenario where self-interactions erase bimodal features.
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Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum
A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.
- Lyman-$\alpha$ forest constraints on pure and mixed fuzzy dark matter