CDS&E: Detection, Instance Segmentation, and Classification for Astronomical Surveys with Deep Learning (DeepDISC)

CDS

• 批准号: 2308174
• 负责人: Xin Liu
• 金额: $ 48.88万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308174&HistoricalAwards=false
• 关键词: CDS&E; Detection; Instance; Segmentation; Classification

The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images of the sky through the 2020s and beyond. As both the sensitivity and depth increase, larger numbers of blended (overlapping) sources will occur. Blending would result in biased measurements, contaminating key astronomical inferences. Having efficient deblending techniques is thus a high priority for the future of astronomical research. However, an efficient and robust method to detect, deblend, and classify sources is still lacking for massive surveys. In this project, scientists at the University of Illinois, Urbana-Champaign will develop a versatile deep learning framework for image deblending and source detection. This work will make it easy to efficiently process wide-deep survey images and will accurately identify blended sources with the lowest possible latency to maximize science returns. Moreover, this work will provide robust uncertainties of detection inferences, which are critical for enabling precision cosmology. The proposed work has broad implications for a wide range of subjects, including detecting transients and solar system objects to probing the nature of dark matter and dark energy. As part of this project, the PI will also develop and teach a dedicated summer outreach program to engage young girls in STEM.This research program will leverage the rapidly developing field of computer vision to build a new deep learning platform for astronomical object detection, instance segmentation, classification, and beyond. It will adapt the latest open-source algorithms in computer vision for object detection and segmentation. The approach is interdisciplinary, combining state-of-the-art astronomical survey data with the latest deep learning tools. The new platform will be trained and validated using a hybrid of real data and realistic simulations that are built by combining traditional image simulations with generative models. It will be fully featured to enable higher-level downstream science applications such as photometric redshift estimation and galaxy morphology inferences. All codes generated will be open source to enable broad community usage.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

下一代广域深度天文勘测将在 2020 年代及以后提供数量空前的天空图像。随着灵敏度和深度的增加，将会出现大量的混合（重叠）源。混合会导致测量结果出现偏差，从而影响关键的天文推论。因此，拥有高效的去混合技术是未来天文学研究的重中之重。然而，大规模调查仍然缺乏有效且稳健的方法来检测、去混合和分类源。在这个项目中，伊利诺伊大学厄巴纳-香槟分校的科学家将开发一种用于图像去混合和源检测的多功能深度学习框架。这项工作将使有效处理宽深度调查图像变得容易，并将以尽可能低的延迟准确识别混合源，以最大限度地提高科学回报。此外，这项工作将提供检测推断的强大不确定性，这对于实现精确宇宙学至关重要。拟议的工作对广泛的学科具有广泛的影响，包括检测瞬变和太阳系物体以探测暗物质和暗能量的性质。作为该项目的一部分，PI 还将开发和教授一个专门的夏季外展项目，以吸引年轻女孩参与 STEM。该研究项目将利用快速发展的计算机视觉领域，构建一个新的深度学习平台，用于天文物体检测、实例分割、分类等。它将采用计算机视觉领域最新的开源算法进行对象检测和分割。该方法是跨学科的，将最先进的天文调查数据与最新的深度学习工具相结合。新平台将使用真实数据和真实模拟的混合体进行训练和验证，这些真实模拟是通过将传统图像模拟与生成模型相结合而构建的。它将功能齐全，以实现更高级别的下游科学应用，例如光度红移估计和星系形态推断。生成的所有代码都将开源，以实现广泛的社区使用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Detection, instance segmentation, and classification for astronomical surveys with deep learning ( deepdisc ): detectron2 implementation and demonstration with Hyper Suprime-Cam data

使用深度学习 (deepdisc) 进行天文测量的检测、实例分割和分类：使用 Hyper Suprime-Cam 数据进行 detectorron2 实现和演示

• DOI: 10.1093/mnras/stad2785
• 发表时间: 2023
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Merz, Grant;Liu, Yichen;Burke, Colin J.;Aleo, Patrick D.;Liu, Xin;Carrasco Kind, Matias;Kindratenko, Volodymyr;Liu, Yufeng
• 通讯作者: Liu, Yufeng