{
  "abstract": "We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy-galaxy strong lensing images. By integrating synthetic Spectral Energy Distribution (SEDs), the pipeline accurately models the redshift-dependent photometric properties of lens and source galaxies, ensuring physical consistency across multi-band observations. The framework incorporates key observational parameters, including Point Spread Functions (PSF), magnitude limits, and zero points, to replicate specific survey conditions, thereby enabling robust cross-survey joint analyses. As an application, we simulate multi-band images for KiDS, LSST, and Euclid using identical lens model parameters, and employ a deep learning network to evaluate image deblending performance. In particular, the simulation leverages PyTorch to ensure full auto-differentiability and GPU acceleration, making it a highly efficient tool for advanced deep learning algorithms that require gradient-based optimization beyond standard model training. Our framework achieves a speedup of approximately $\\mathcal{O}(10^3)$ over traditional CPU-based pipelines, demonstrating the potential feasibility of joint gradient-based lens modeling across next-generation surveys.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.18180",
  "author_total": 6,
  "authors": [
    {
      "name": "Fucheng Zhong"
    },
    {
      "name": "Ruibiao Luo"
    },
    {
      "name": "Nicola R. Napolitano"
    },
    {
      "name": "Crescenzo Tortora"
    },
    {
      "name": "Valerio Busillo"
    },
    {
      "name": "Rui Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2jqgvf4k6ahkv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.18180",
  "fulltext_url": null,
  "id": "2jqgvf4k6ahkv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.18180",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.18180",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2jqgvf4k6ahkv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "2609.18180",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.18180"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    }
  ],
  "title": "Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands",
  "title_text": "Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/2jqgvf4k6ahkv",
  "venue": "arXiv",
  "versions": []
}