Research on Parallel Algorithms for uv-faceting Imaging

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility.     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.     

气候系统预测:基础创新和集成应用

随着全球变暖,极端天气气候事件增强,天气气候灾害造成的损失也愈发严重。当前气候预测的准确性远远不能满足社会需要,气候系统预测理论和方法面临着众多挑战性问题。为提档气候预测科学水平和准确率,由南京信息工程大学和中山大学承担的“气候系统预测研究中心”获得国家自然科学基金基础科学中心项目支持(2021年1月—2025年12月)。在该项目执行的前三年,项目团队开展了大量深入系统的研究,并取得了若干重要进展:1)揭示了气候系统的若干关键变化、驱动力和机制;2)剖析了海-陆-冰-气相互作用对我国重大极端气候事件的影响;3)在气候系统数值模式研发和预测系统集成方面取得重要进展;4)发展了延伸期-S2S-年代际的气候系统预测理论和方法。本文对这些进展作了扼要介绍,并针对气候与环境变化归因、古今气候环境研究融合、跨时空气候系统变异和极端气候、人工智能与气候科学、年代际预测和风险应对体系等关键科学问题做了展望。     

柴达木盆地南八仙地区渐新世浅水三角洲物源及沉积演化

以古近系渐新统下干柴沟组上段(E23)为研究对象,在精细岩芯观察和钻(测)井资料综合分析的基础上,结合重矿物组合、ZTR指数、砂岩碎屑组分、粒度累积概率图和砂地比等参数综合分析结果,对柴达木盆地南八仙地区E23湖盆扩张期弱退积型浅水三角洲物源方向、砂体展布规律和沉积微相发育演化进行了深入研究。结果表明：① 南八仙地区E23沉积时期物源主要来自南祁连山中酸性岩浆岩和中高级变质岩发育区,物源方向为近北东—南西向,稳定重矿物(磁铁矿、石榴子石、白钛矿和锆石)含量高(质量分数介于81. 0%~99. 9%)、不稳定重矿物(绿帘石和角闪石)含量低。② 南八仙地区E23沉积时期为浅水三角洲—滨浅湖沉积环境,主要发育水下分流河道砂体,岩性主要为岩屑长石砂岩,其次为长石岩屑砂岩,砂体的成分成熟度一般中等—较差、分选磨圆度中等—较差,粒度概率图以反映河流作用的一跳跃一悬浮式为主;沉积构造类型多样,主要发育块状层理、正粒序层理、平行层理和槽状交错层理。③ E23沉积时期,南八仙地区继承性发育3条北东—南西向呈条带状展布的砂体富集带(砂地比值介于35%~65%之间),早期到中期砂体发育程度增强,呈进积式;中期到晚期砂体发育程度有所减弱,呈弱退积式。④ E23沉积时期,南八仙地区处于浅水三角洲前缘亚相沉积环境,持续发育3个呈北东—南西向展布的沉积朵叶体,主要发育水下分流河道微相,其次为水下分流间湾微相,早期到中期浅水三角洲前缘发育规模增大,中期到晚期发育规模减小。     

Monopole abundance from first-order GUT phase transition of the early Universe

Monopole annihilation and pair creation are investigated in a first order GUT symmetry breaking phase transition without inflation. It is found that even with complete initial monopole suppression, some monopoles are inevitably produced during the transition. According to this, some constraints are obtained for initial and equilibrium monopole densities in order to be compatible with the present observational limits even without inflation.     

气候变化和生态建设对秦岭—淮河南北植被动态的影响

论文基于2000-2019年秦岭—淮河南北MODIS-NDVI植被覆盖数据,对中国南北过渡带植被时空变化进行分析,并探讨植被动态变化驱动因素.结果 表明:①在趋势变化上,2000-2019年秦岭—淮河南北植被显著恢复.其中,秦巴山区植被恢复面积占比最高,其次是巫山山区和关中平原;植被退化区面积占比仅为6.4％,主要分布...     

气候变化对植物多样性的影响研究综述

植物多样性是生态系统结构和功能的基础,气候变化已对其产生了深刻的影响,甚至于严重威胁.植物多样性的减少或丧失必然会影响生态系统结构和功能的稳定性,导致严重的生态、经济和社会后果.植物多样性对气候变化的响应受控于气温和水分的动态平衡.本文系统总结了气候变暖和降水格局变化对植物多样性的影响,分析了气候变化对不同生态系统、群...     

德保县农业气候资源的保护对策

针对目前德保县在农业气候资源利用中的存在问题,提出保护当地农业气候资源的几点应对策略。     

延庆发展奶牛的气候优势

依据奶牛生物学特性及与气象条件关系,利用延庆地区30年的气候资料,从地理位置、体高、适温条件、不适温条件、温湿指数、相对湿度和日照、风寒指数、饲料条件等多方面,分析论证了延庆在发展奶牛相对于北京平原地区的气候优势.综合分析表明,相对于北京平原地区,延庆气候更适于发展奶牛养殖.     

1月北大西洋东部-乌拉尔山阻塞高压频次年际尺度偶极子模态特征

基于观测和再分析资料,本文研究了近几十年来1月北大西洋东部-乌拉尔山阻塞高压频次年际变异主导模态特征及与其相联的大气背景场。结果表明,1980—2019年冬季(12月—次年2月)该地区阻塞高压频次年际变异的主导模态存在明显月际差异:12月表现为北大西洋东部-欧洲西部地区阻塞高压频次的显著同位相变化,1月为北大西洋东部-欧洲西部与乌拉尔山地区阻塞高压频次的具有显著的反位相变化即偶极子模态,2月则为北大西洋东部-乌拉尔山阻塞高压频次的显著同位相变化。进一步研究表明:1月北大西洋东部-乌拉尔山阻塞高压频次偶极子模态与同期局地纬向西风、纬向风垂直切变、经向位涡梯度等大气背景场异常偶极型变化相联系。当乌拉尔山地区关键大气背景场为负异常,而北大西洋东部-欧洲西部为正异常时,两个地区阻塞高压频次分别增加和减少,呈现偶极子模态;反之亦然。阻塞高压频次偶极子模态及相关的背景环流异常可通过调节水平温度平流、垂直运动和水汽输送等来影响1月欧亚北部气温和降水,以及它们的极端事件频次。当阻塞高压频次偶极子模态处于正位相时,乌拉尔山北部(南部)和欧洲南部增温(降温),极端暖、冷事件频次分别增加(减少)和减少(增加...