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Light-curve data are one of the most important data sources in time domain astronomy research. With the advancement of observation facilities and the continuous accumulation of observation data, and considering the analysis needs of large sample data sets, software or tools based on new technologies, especially artificial intelligence (AI), will be indispensable for light-curve analysis. The light-curve analysis tool designed by an individual will follow its own defined data structure, which will make the tools designed and developed by different individuals incompatible. A unified light-curve data model will be able to solve this compatibility problem, similar to the traditional flexible image transport system (FITS) file format. This paper proposes a light-curve data model named TSCat, designs and implements a data storage engine. The TSCat data model defines the basic metadata and format required for storing optical curve data following the international virtual observatory alliance (IVOA) data format specification system. The TSCat storage engine implements basic operations such as importing, storing, and accessing light-curve data. The function and performance of the TSCat storage engine are evaluated through the actual observation data. The experimental results show that the TSCat data model is complete enough to support the analysis of light curves. TSCat will help support the standardization of the data access level of software and tools in the field of light-curve analysis, and provide a new reference for the specification system of astronomical scientific data formats.

Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues,is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical cataloguesfrom survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties., Comment: Accepted for publication in MNRAS. 14 pages, 9 figures, 11 tables

Astronomy education and public outreach (EPO) is one of the important part of the future development of astronomy. During the past few years, as the rapid evolution of Internet and the continuous change of policy, the breeding environment of science EPO keep improving and the number of related projects show a booming trend. EPO is no longer just a matter of to teachers and science educators but also attracted the attention of professional astronomers. Among all activates of astronomy EPO, the data driven astronomy education and public outreach (abbreviated as DAEPO) is special and important. It benefits from the development of Big Data and Internet technology and is full of flexibility and diversity. We will present the history, definition, best practices and prospective development of DAEPO for better understanding this active field.

Correlating BASS DR3 catalogue with ALLWISE database, the data from optical and infrared information are obtained. The quasars from SDSS are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost and Random forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to firstly classify the data into low- and high- redshift datasets, and then predict photometric redshifts of these two datasets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random forest. For two-step model, the performance of these three algorithms on the classification of low- and high-redshift subsamples are compared, and CatBoost still shows the best performance. Therefore CatBoost is regard as the core algorithm of classification and regression in two-step model. By contrast with one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high redshift quasars. Finally the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high redshift quasar candidates is 3938 (redshift $\ge 3.5$) and 121 (redshift $\ge 4.5$) by two-step model. The predicted result will be helpful for quasar research and follow up observation of high redshift quasars., 16 pages, 8 figures, 12 tables, accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

We are at the threshold of an era with unprecedented opportunities for cross-disciplinary and cross-border research collaborations. Open Science that started years ago and is growing in momentum with the digitalization and many big investments such as those made by the European Commission. Open Science Cloud, Platform, Commons are developed at national and regional level. It is very timely for developing Global Open Science Cloud (GOSC) that aims to connect the different international, national and regional research infrastructures to create a global digital environment for borderless research and innovation. Fundamentally, it aims to make it easy for scientists to access cross-continent digital resources in order to resolve global challenges such as problems caused by the COVID19, and those identified in the UN sustainable Development Goals. This report discusses the concept and landscape of GOSC. It is a major outcome from a workshop during EGI Conference 2020 that gathered global digital infrastructures, scientific communities, policy makers and funders to review the existing work, examine the available resources, and identify collaboration opportunities. This report is among the initial publications that document GOSC discussions with the aim to inform future developments.

Over the past year, the discovery of the first electromagnetic counterparts to sources of gravitational waves and high-energy neutrinos has brought us to the new era of ”multi-messenger” astrophysics (MMA). These new events provide deep insights into astrophysics, meanwhile, lead to the development of astronomical data fusion techniques. Each messenger carries information from different observation process and fusing all messengers can therefore provide the best understanding of the celestial objects’ attributes and expose various astrophysical phenomena. Over the decades, for the full exploitation of multi-band astronomical data, advanced unified platforms and data fusion techniques have been developed and become a research topic of interest for the astronomical community. In this paper, we present a systematic review of the popular and state-of-the-art fusion methods and platforms in different formats of astronomical data, aiming to give a comprehensive introduction to the current progress in this field; at the same time, we put forward insightful prospects for future study of multi-messenger data fusion, hoping to provide new thoughts for researchers in MMA era.

Astronomical observation data require long-term preservation, and the rapid accumulation of observation data makes it necessary to consider the cost of long-term archive storage. In addition to low-speed disk-based online storage, optical disk or tape-based offline storage can be used to save costs. However, for astronomical research that requires historical data (particularly time-domain astronomy), the performance and energy consumption of data-accessing techniques cause problems because the requested data (which are organized according to observation time) may be located across multiple storage devices. In this study, we design and develop a tool referred to as AstroLayout to redistribute the observation data using spatial aggregation. The core algorithm uses graph partitioning to generate an optimized data placement according to the original observation data statistics and the target storage system. For the given observation data, AstroLayout can copy the long-term archive in the target storage system in accordance with this placement. An efficiency evaluation shows that AstroLayout can reduce the number of devices activated when responding to data-access requests in time-domain astronomy research. In addition to improving the performance of data-accessing techniques, AstroLayout can also reduce the storage system’s power consumption. For enhanced adaptability, it supports storage systems of any media, including optical disks, tapes, and hard disks.

The WorldWide Telescope(WWT) is a scientific visualization platform which can browse deep space images, star catalogs, and planetary remote sensing data from different observation facilities in a three-dimensional virtual scene. First launched and then open-sourced by Microsoft Research, the WWT is now managed by the American Astronomical Society (AAS). Hierarchical Progressive Survey (HiPS) is an astronomical data release scheme proposed by Centre de Donn\'ees astronomiques de Strasbourg (CDS) and has been accepted as a recommendation by International Virtual Observatory Alliance (IVOA). The HiPS solution has been adopted widely by many astronomical institutions for data release. Since WWT selected Hierarchical Triangular Mesh (HTM) as the standard for data visualization in the early stage of development, data released by HiPS cannot be visualized in WWT, which significantly limits the application of WWT. This paper introduces the implementation method for HiPS dataset visualization in WWT, and introduces HiPS data projection, mesh rendering, and data index implementation in WWT. Taking Chang'E-2 lunar probe data as an example, this paper introduces how to convert planetary remote sensing data into a HiPS dataset and integrate it into WWT. This paper also compares the efficiency and memory consumption of WWT loading its native data and HiPS data, and illustrates the application of HiPS in scientific data visualization and science education in WWT., Comment: 22 pages, 15 figures

Public observatory is playing more and more important role in astronomy education, citizen science and scientific research. It is developing rapidly and has made some progress through a variety of approaches. As relative small number and high operating costs of professional observatories, it leads that only just a few students benefit. As a result, amateur astronomers begin to build their own remote observatories, but they also face great challenge, especially in hardware technology experience and construction funds. Their self-designed hardware connection and control system often breaks down, and then a stable hardware integration system becomes very critical. According to requirement investigation to dozens of the amateur, we propose an amateur public observatory model, and a hardware integration system as a bridge between observatory equipment and observation software. It not only can satisfy hardware requirement for multiple control modes, but also saves cost. It has great significance in promoting citizen science and astronomy education.

Radio Frequency Interference (RFI) is a type of inevitable noise in the radio astronomy data collection process. It can corrupt weak cosmic signals and potentially lead to misleading results. The proper identification of RFI during data processing thus is critical to obtain clean and high-quality data for analysis. This need will become even more urgent when next generation of large radio telescopes, such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST) comes into service and generates an increasing amount and complexity of radio signal data. Among RFI identification methods, detection using artificial intelligence (AI) has particularly demonstrated advantages in superior efficiency, accuracy and less human intervention. We thus propose a RFI detection model based on Pix2Pix , an image-to-image translation solver using Generative Adversarial Network (GAN): RFI-GAN. This model transforms the RFI detection to an image translation problem and trains two deep neural networks that contest each other to output a binary RFI mask image to eliminate RFI noises. We also optimize the network structures of the generator and discriminator used in the Pix2Pix model for better quality of RFI detection, making it suitable for processing data from single antenna radio telescope. The model is designed to serve the upcoming FAST data and has been evaluated using a standard simulation data set generated by the HI Data Emulator ( HIDE ). Experimental results have shown that our model can achieve higher scores (99%) on accuracy, recall and F1-score than other RFI detection methods.

With the development of modern science and technology, astronomy has entered the era of data-intensive scientific discovery. Cyber-infrastructure is becoming a crucial part of both key national science projects and daily research in astronomy. In this chapter, cyber-infrastructure requirements and implementation status of five cutting-edge astronomical projects are described, namely the five-hundred-meter aperture spherical radio telescope (FAST), the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), the MingantU SpEctral Radioheliograph (MUSER), the Chinese–French SVOM mission, and the Square Kilometer Array (SKA). Furthermore, as a common cyber-infrastructure for astronomy research, a brief overview of Virtual Observatory including its research and development history and current applications is given. Several suggestions are listed in the end of the paper for further development of scientific research cyber-infrastructure.

Efficiently retrieving a subset of time series data in a specific sky region from large-scale astronomical observation data is important for some time-domain studies in astronomy, especially for research on long-term variable sources. Considering that the field of view of a telescope is generally larger than the effective sky region, the retrieval method at the full image level is relatively inefficient in terms of data transmission and storage. Therefore, this paper proposes a search engine dedicated to the efficient retrieval of time-series subimages in observed image data archives (Astro-TS3, Astronomical Time-Series Subimage Search Engine). An in situ and resilient indexing scheme is proposed, and it can build indexes for archived image data without any modification of the original files and support incremental updating with newly archived data. A subimage cutting tool is implemented to extract a part of the image according to the range specified by the user and generate a new Flexible Image Transport System (FITS) file for the extracted subimage. With Astro-TS3, users only need to submit requests including the ranges of the time and sky region to retrieve the required data and then download the time-series subimages after the preview. The evaluation of Astro-TS3 is performed using actual archived data from three Antarctic Survey Telescopes (AST3). The experimental data show that the time for the index establishment and incremental update increases approximately linearly relative to the number of data files. For retrieval, the response time of the data retrieval is approximately linearly related to the number and size of the subimages.

Astronomy has always been, and will continue to be, a data-based science, and astronomers nowadays are faced with increasingly massive datasets, one key problem of which is to efficiently retrieve the desired cup of data from the ocean. AQUAdexIM, an innovative spatial indexing and querying method, performs highly efficient on-the-fly queries under users’ request to search for Time Series Images from existing observation data on the server side and only return the desired FITS images to users, so users no longer need to download entire datasets to their local machines, which will only become more and more impractical as the data size keeps increasing. Moreover, AQUAdexIM manages to keep a very low storage space overhead and its specially designed in-memory index structure enables it to search for Time Series Images of a given area of the sky 10 times faster than using Redis, a state-of-the-art in-memory database.

Astronomy cloud computing environment is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation.

The search and follow-up observation of electromagnetic (EM) counterparts of gravitational waves (GW) is a current hot topic of GW cosmology. Due to the limitation of the accuracy of the GW observation facility at this stage, we can only get a rough sky-localization region for the GW event, and the typical area of the region is between 200 and 1500 square degrees. Since GW events occur in or near galaxies, limiting the observation target to galaxies can significantly speedup searching for EM counterparts. Therefore, how to efficiently select host galaxy candidates in such a large GW localization region, how to arrange the observation sequence, and how to efficiently identify the GW source from observational data are the problems that need to be solved. International Virtual Observatory Alliance has developed a series of technical standards for data retrieval, interoperability and visualization. Based on the application of VO technologies, we construct the GW follow-up Observation Planning System (GWOPS). It consists of three parts: a pipeline to select host candidates of GW and sort their priorities for follow-up observation, an identification module to find the transient from follow-up observation data, and a visualization module to display GW-related data. GWOPS can rapidly respond to GW events. With GWOPS, the operations such as follow-up observation planning, data storage, data visualization, and transient identification can be efficiently coordinated, which will promote the success searching rate for GWs EM counterparts., Comment: 12 pages, 8 figures, published by Publications of the Astronomical Society of the Pacific

Virtual Observatory (VO) is a data-intensively online astronomical research and education environment, which takes advantages of advanced information technologies to achieve seamless and global access to astronomical information. AstroCloud is a cyber-infrastructure for astronomy research initiated by Chinese Virtual Observatory (China-VO) project, and also a kind of physical distributed platform which integrates lots of tasks such as telescope access proposal management, data archiving, data quality control, data release and open access, cloud based data processing and analysis. It consists of five application channels, i.e. observation, data, tools, cloud and public and is acting as a full lifecycle management system and gateway for astronomical data and telescopes. Physically, the platform is hosted in six cities currently, i.e. Beijing, Nanjing, Shanghai, Kunming, Lijiang and Urumqi, and serving more than 17 thousand users. Achievements from international Virtual Observatories and Cloud Computing are adopted heavily. In the paper, backgrounds of the project, architecture, Cloud Computing environment, key features of the system, current status and future plans are introduced., Comment: 5 pages, 8 figures, to appear in the Proceedings of "The 3rd International Conference on Wireless Communication and Sensor Network (WCSN 2016)"

In this work, statistical analyses are conducted to measure the stacking fault density and stacking fault energy in a Ni–Co-based superalloy at different deformation temperatures and strain rates. High stacking fault density and low stacking fault energy indicate that the dynamic strain aging effects are caused by Suzuki segregation, which is enhanced by serious intersections of stacking faults in the superalloy. These results provide new insight into understanding stacking fault effects on dynamic strain aging in engineering alloys.

Macroscopically zero-strained twin lamellae are observed in a coarse-grained Ni–Co-based superalloy after plastic deformation. The twin lamellae presented as three layers of overlapping stacking faults with zero overall Burgers vector. The atomic stacking sequence in the twin lamellae is consistent with pairs of parallel stacking faults at neighboring or next-neighboring glide planes. The twinning is facilitated by the low stacking fault energy of the superalloy and the lengthening of stacking faults by shear stress from nearby stacking faults.

Section 1 of the FM14 focus on bridging the astronomy research and outreach communities - recent highlights, emerging collaborations, best practices and support structures. This paper also contains supplementary materials that point to contributed talks and poster presentations that can be found online.

The Burst Observer and Optical Transient Exploring System (BOOTES), is a global robotic observatory network, which started in 1998 with Spanish leadership devoted to study optical emissions from gamma ray bursts (GRBs) that occur in the Universe. We present shot history and current status of BOOTES network. The Network philosophy, science and some details of 117 GRBs followed-up are discussed.