Subjects: Astronomy submitted time 2023-10-07 Cooperative journals: 《天文学报》
Abstract： Affected by a large number of radio frequency interference signals, it has become an important task for astronomical data processing to quickly and accurately identify single pulse signals from massive observation data. Designing and extracting effective data features is the key issue for efficient identification of single pulse signals using machine learning. This paper proposes an ensemble feature selection method for single pulse signal classification. The method first mixed three types of features, including the parametric features, statistical features and abstract features of single pulse signals, and then used five independent feature selection methods to select the corresponding optimal feature set, respectively. At last, the features selected by the five independent methods are mixed and the greedy strategy was used to select the optimal ensemble feature set. The experimental results show that the ensemble feature set can improve F1-score by value of 1.8\% at most and can obtain higher accuracy than the features selected by independent methods. Under the background of high-speed and large-scale sky survey, the ensemble feature selection method plays an important role in reducing the number of features, improving classification performance and speeding up data processing.
Subjects: Astronomy >> Astrophysical processes submitted time 2019-07-30 Cooperative journals: 《天文研究与技术》
Abstract：经过十余年的不断建设和发展，中国虚拟天文台（China-VO）已成为支撑天文学观测、研究、教学的重要技术和资源平台。随着多信使天文学和时域天文学时代的到来，虚拟天文台也需要升级自身的核心能力，以给天文工作者提供更精准的服务和技术支撑。为此，中国虚拟天文台团队结合天文学的发展方向和信息技术发展趋势梳理了一份核心技术需求清单，并以问卷的形式针对领域内专家和用户开展了调研。通过对调研结果的统计和分析，中国虚拟天文台明确了未来一段时期的主要努力方向和目标，计划采用平台化的开发模式，并开放第三方开发接口，以吸引更多感兴趣的开发者基于虚拟天文台资源做出实用的工具，更好地实现资源与技术向服务的快速转换。
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