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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.03691 |
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| _version_ | 1866911567722840064 |
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| author | Feng, Xiaotang Wang, Zihan Shu, Zilang Kneib, Jean-Paul Torr, Philip |
| author_facet | Feng, Xiaotang Wang, Zihan Shu, Zilang Kneib, Jean-Paul Torr, Philip |
| contents | Probing dark matter distribution on sub-galactic scales is essential for testing the Cold Dark Matter ($Λ$CDM) paradigm. Strong gravitational lensing, as one of the most powerful approach by far, provides a direct, purely gravitational probe of these substructures. However, extracting cosmological constraints is severely bottlenecked by the mass-sheet degeneracy (MSD) and the unscalable nature of manual and neural-network modeling. Here, we introduce LensAgent, a pioneering training-free, large language model (LLM)-driven agentic framework for the autonomous physical inference of mass distributions. Operating as an autonomous scientific agent, LensAgent couples high-level logical reasoning with deterministic physical modeling tools, demonstarting successful reconstruction of mass distribution in SLACS Grade A strong lensing systems. This self-evolving architecture enables the robust extraction of sub-galactic substructures at scale, unlocking the cosmological potential of upcoming wide-field surveys such as the Rubin Observatory (LSST) and Euclid. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03691 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LensAgent: A Self Evolving Agent for Autonomous Physical Inference of Sub-galactic Structure Feng, Xiaotang Wang, Zihan Shu, Zilang Kneib, Jean-Paul Torr, Philip Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Probing dark matter distribution on sub-galactic scales is essential for testing the Cold Dark Matter ($Λ$CDM) paradigm. Strong gravitational lensing, as one of the most powerful approach by far, provides a direct, purely gravitational probe of these substructures. However, extracting cosmological constraints is severely bottlenecked by the mass-sheet degeneracy (MSD) and the unscalable nature of manual and neural-network modeling. Here, we introduce LensAgent, a pioneering training-free, large language model (LLM)-driven agentic framework for the autonomous physical inference of mass distributions. Operating as an autonomous scientific agent, LensAgent couples high-level logical reasoning with deterministic physical modeling tools, demonstarting successful reconstruction of mass distribution in SLACS Grade A strong lensing systems. This self-evolving architecture enables the robust extraction of sub-galactic substructures at scale, unlocking the cosmological potential of upcoming wide-field surveys such as the Rubin Observatory (LSST) and Euclid. |
| title | LensAgent: A Self Evolving Agent for Autonomous Physical Inference of Sub-galactic Structure |
| topic | Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2604.03691 |