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CNN+FoF application of deep learning to the identification of dark matter haloes

S Maiti, CM Correa, A Fiorilli, AN Ruiz, DJ Paz… - arXiv preprint arXiv …, 2026 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… halo finders, achieving a speed-up of approximately one order of magnitude relative to ROCKSTAR, offering a promising pathway for modern simulation-based inference …

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BibTeX

@article{2602.21246v1,
Author        = {Soumadeep Maiti and Carlos M. Correa and Andrea Fiorilli and Andrés N. Ruiz and Dante J. Paz and Alejandro Pérez Fernández and Ariel G. Sánchez},
Title         = {CNN+FoF: application of deep learning to the identification of dark matter haloes},
Eprint        = {2602.21246v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo or non-halo members, followed by a highly optimised and parallelised Friends-of-Friends clustering algorithm that groups the classified halo members into distinct haloes. The training data comprise simulations generated using GADGET-4, with labels obtained with the ROCKSTAR halo finder. Our models incorporate two main halo mass definitions, $M_{200\mathrm{b}}$ and $M_{\text{vir}}$, with similar performance. For haloes defined by the ROCKSTAR $M_{200\mathrm{b}}$ criterion, the classification network demonstrated stable performance across multiple simulation resolutions. For the highest resolution, it achieved over $98\%$ across all primary performance metrics when identifying halo particles. Furthermore, the FoF algorithm yielded halo catalogues with a purity generally exceeding $95\%$ and a stable completeness of $93\%$ for masses above $5\times10^{11} \, M_\odot$. Our pipeline recovered the centre-of-mass positions, velocities and halo masses with high fidelity, yielding a halo mass function consistent to within $5\%$ of the reference while faithfully reconstructing the internal density profiles. The primary objective of this study is to offer a faster and scalable alternative to conventional halo finders, achieving a speed-up of approximately one order of magnitude relative to ROCKSTAR, offering a promising pathway for modern simulation-based inference methods that rely on rapid and accurate structure identification.},
Year          = {2026},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2602.21246v1},
File          = {2602.21246v1.pdf}
}

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