您选择的条件: Yu Song
  • Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers

    分类: 天文学 >> 天文学 提交时间: 2023-02-19

    摘要: Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.

  • Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers

    分类: 天文学 >> 天文学 提交时间: 2023-02-19

    摘要: Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.

  • Research on Optimization of Supply Chain Management of Private Publishing Companies under the New Development Philosophy Take the Dook Media Group as An Example

    分类: 工程与技术科学 >> 工程与技术科学其他学科 提交时间: 2023-01-03 合作期刊: 《2022年第三届艺术设计、传播与工程科学研讨会》

    摘要: In recent years, with the participation of emerging technologies, multi-party capital competition, and changes in audience reading habits, the publishing environment has shaped a new field, which revolutionized the production process and thinking paradigm of publishing. In this context, the optimization of supply chain management in the publishing industry can be regarded as a considerable measure to improve the quality and efficiency of the publishing industry. Combining the new development concept of innovation, coordination, green, openness and sharing with the supply chain management of the publishing industry, to discuss the optimization of the supply chain management of the publishing industry under the background of the new development philosophy.