POLARIS: A High-contrast Polarimetric Imaging Benchmark Dataset for Exoplanetary Disk Representation Learning

• URL: http://arxiv.org/abs/2506.03511v1
• Date: Wed, 04 Jun 2025 02:55:02 GMT
• Title: POLARIS: A High-contrast Polarimetric Imaging Benchmark Dataset for Exoplanetary Disk Representation Learning
• Authors: Fangyi Cao, Bin Ren, Zihao Wang, Shiwei Fu, Youbin Mo, Xiaoyang Liu, Yuzhou Chen, Weixin Yao,
• Abstract summary: With over 1,000,000 images from more than 10,000 exposures, can artificial intelligence (AI) serve as a transformative tool in imaging Earth-like exoplanets in the coming decade?<n>We introduce a benchmark and explore this question from a polarimetric image representation learning perspective.<n>This is the first uniformly reduced, high-quality exoplanet imaging dataset, rare in astrophysics and machine learning.
• Score: 27.870701240010924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With over 1,000,000 images from more than 10,000 exposures using state-of-the-art high-contrast imagers (e.g., Gemini Planet Imager, VLT/SPHERE) in the search for exoplanets, can artificial intelligence (AI) serve as a transformative tool in imaging Earth-like exoplanets in the coming decade? In this paper, we introduce a benchmark and explore this question from a polarimetric image representation learning perspective. Despite extensive investments over the past decade, only a few new exoplanets have been directly imaged. Existing imaging approaches rely heavily on labor-intensive labeling of reference stars, which serve as background to extract circumstellar objects (disks or exoplanets) around target stars. With our POLARIS (POlarized Light dAta for total intensity Representation learning of direct Imaging of exoplanetary Systems) dataset, we classify reference star and circumstellar disk images using the full public SPHERE/IRDIS polarized-light archive since 2014, requiring less than 10 percent manual labeling. We evaluate a range of models including statistical, generative, and large vision-language models and provide baseline performance. We also propose an unsupervised generative representation learning framework that integrates these models, achieving superior performance and enhanced representational power. To our knowledge, this is the first uniformly reduced, high-quality exoplanet imaging dataset, rare in astrophysics and machine learning. By releasing this dataset and baselines, we aim to equip astrophysicists with new tools and engage data scientists in advancing direct exoplanet imaging, catalyzing major interdisciplinary breakthroughs.

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