StarWhisper Telescope: an AI framework for automating end-to-end astronomical observations

<p>Non-professional astronomers can significantly contribute to key research projects in Astronomy. The collaboration between professional and amateur astronomers is known as ProAm collaboration. Since 2009 the Spanish Astronomy Association (SEA) has housed a specific working group promoting the relationship between professional and amateur astronomers: the ProAm Commission.</p> <p>The SEA ProAm Commission has just published a report analyzing the status of the ProAm collaboration in Spain. It is the first time that such analysis has been performed in Spain.</p> <p>Data were collected using the information provided in recent amateur astronomy conferences. An exhaustive search of scientific publications in which amateur astronomers had participated was also carried out. And a survey to both amateur and professional astronomers was conducted to compile key information.</p> <p>This report confirms that Spanish amateur astronomers collaborate significantly with professional astrophysicists, with around a 100 of them being regularly included in science publications and astronomical circulars. The number of ProAm collaborations is steadily increasing with time. More than 200 peer-reviewed publications and almost 5000 astronomical circulars including Spanish amateur astronomers have been published to date. However the fraction of women involved in ProAm collaborations is low: 4% and 7% for the professional and amateur astronomers, respectively. This significant gender gap needs to be addressed.</p> <p>Both amateur and professional astronomers requested to improve communication channels in both directions. The SEA ProAm Commission is addressing this issue with the development of a specific webpage, https://proam.sea-astronomia.es .</p> <p>In addition, since 2021 the SEA ProAm Commission organizes informative sessions on a monthly basis with the aim of promoting the visibility of the ProAm collaboration in Spain.</p> <p>The SEA ProAm Commission is also coordinating several training courses to increase the observing and data processing skills of amateur astronomers. So far two courses have been held: 'Python Course for amateur astronomers' and the 'Astronomical Calculation Course'. All the training courses (material and recordings of the sessions) and the dissemination sessions of the ProAm projects are publically available in the SEA ProAm Commission webpage.</p> <p>Finally, we have defined a code of good conduct applicable to ProAm collaborations and relationships in astronomy, where the rules and good practices to be followed are brought together to guarantee the well-being of all the people who participate in ProAm projects.</p>

In the field of visual double stars, a long term follow-up is required, since their orbital periods may reach several centuries. Created in 1981 within the Société Astronomique de France (SAF) with the support of the late Paul Muller (1910-2000), the Commission des Etoiles Doubles provides the framework for the necessary collaboration between professional and amateur astronomers, through generations. The late Dr. Paul Baize (1901-1995) was a model for its members. Several professional astronomers became scientific advisors of the Commission and have guided many works made by amateurs.

Combining neural networks for the near-real-time processing of satellite data

Observations of OH masers of comets in 1.6GHz frequency band using the Irbene RT32 radio telescope

We present an algorithm for the rapid retrieval of the carbon dioxide total column amounts (XCO2) using short wave infrared (SWIR) spectra of the reflected sunlight measured from space. The algorithm takes advantage of the combined processing of observational data from two different satellite missions. For the algorithm implementation we adopted the previously developed EOF (Empirical Orthogonal Functions)-based approach that exploits regression relations of the principal components of the measured spectra with target XCO2 values. In the original algorithm version the regression coefficients were derived by using training sets of collocated satellite and ground-based observations (ground-based observations were treated as “true values”). In this paper we implemented similar approach in which training set for one satellite mission is created using collocated observations of the another “reference” space mission simultaneously on-orbit (in this case XCO2 retrievals of the “reference” mission were treated as “true values”). This approach enables rapid data processing of the new satellite missions omitting expensive and time consuming stage of retrieval algorithm development. The feasibility of the approach was tested by joint processing of GOSAT and OCO-2 observation data. For the analysis of the algorithm precision/accuracy characteristics we used the collocated observations from the Total Carbon Column Observing Network (TCCON).

ABSTRACTThe challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

This research aims to analyze the impact of the Earth-Space link on the Automatic Identification System (AIS) signals of ships. To achieve this, we established a simulation system that measures the receiving power of AIS signals via satellite platforms. We validated the system by utilizing observation data from Tiantuo-5. Through this simulation, we quantitatively analyzed the effects of ionospheric TEC (Total Electron Content) and space loss on the received power. During the processing of observation data, we construct a geometric propagation model utilizing the measured positions of both the satellite and the ship. We then calculate the antenna gain and remove any system errors. Additionally, we eliminate the deviation of elevation and azimuth angles caused by satellite motion. This allows us to determine the actual power of different ships reaching the receiving platform. Upon comparing the measured power data with the simulated power, it was noted that both exhibited an increasing trend as the elevation angle increased. This led to an RMSE (Root Mean Square Error) result of approximately one, indicating the accuracy of the simulation system. These findings hold significant implications for analyzing interference factors in satellite-ground links.

The surge in the number of earth observation satellites being launched worldwide is placing significant pressure on the satellite-direct ground receiving stations that are responsible for systematic data acquisition, processing, archiving, and dissemination of earth observation data. Growth in the number of satellite sensors has a bearing on the ground segment payload data processing systems due to the complexity, volume, and variety of the data emanating from the different sensors. In this paper, we have aimed to present a generic, multi-mission, modularized payload data processing system that we are implementing to optimize satellite data processing from historical and current sensors, directly received at the South African National Space Agency’s (SANSA) ground receiving station. We have presented the architectural framework for the multi-mission processing system, which is comprised of five processing modules, i.e., the data ingestion module, a radiometric and geometric processing module, atmospheric correction and Analysis Ready Data (ARD) module, Value Added Products (VAPS) module, and lastly, a packaging and delivery module. Our results indicate that the open architecture, multi-mission processing system, when implemented, eliminated the bottlenecks linked with proprietary mono-mission systems. The customizable architecture enabled us to optimize our processing in line with our hardware capacities, and that resulted in significant gains in large-scale image processing efficiencies. The modularized, multi-mission data processing enabled seamless end-to-end image processing, as demonstrated by the capability of the multi-mission system to execute geometric and radiometric corrections to the extent of making it analysis-ready. The processing workflows were highly scalable and enabled us to generate higher-level thematic information products from the ingestion of raw data.

Driven by the demand for effective data processing, archive management, quality analysis, and operational services, as well as the requirement that data applications utilize the payloads of Tiangong-2 with various mechanisms that are designed for advanced scientific experiments and earth observation, an integrated and ground-based data processing and service system with comprehensive support capabilities and intuitive technology features was developed. The system enables multisource and automated massive data-parallel processing. It has high-precision and high-quality production and enables efficient data archiving. It also facilitates visual management, data quality analysis, and control, online distribution services, and operation, etc. This paper (1) summarizes the current situation and development trend of ground processing and service systems for space data; (2) analyzes the technical requirements in processing, management, and sharing of space data for Tiangong-2 applications; (3) systematically introduces the overall architecture, operation processes, functions, and interfaces of the ground data system; (4) designs and implements in detail the functions of data -parallel processing and product production, visual-data storage and management, system scheduling and control, data quality analysis and control, and data online sharing services; (5) further analyzes key technical problems of high -precision data processing and concurrent dispatching of business processes for new earth observation payloads; and (6) elaborates the specific solutions. The system has been running steadily for 3 years and has provided more than 48 TB data products for researchers around the world. The ground data-processing system has played an important role in supporting scientific researches, outputs, and application promotion in various application fields of China Manned Space Engineering.

<p>The Europlanet 2024 Research Infrastructure (RI) provides free access to the world’s largest collection of planetary simulation and analysis facilities, data services and tools, a ground-based observational network and programme of community support activities (https://www.europlanet-society.org/europlanet-2024-ri/). </p> <p>A new collaboration between telescopes around the world has been launched in 2020 to provide coordinated observations and rapid responses in support of planetary research from space missions and in follow-ups of new events. The <strong>Europlanet Telescope Network (EPN-TN)</strong> is providing professional and trained amateur observers with access to small and medium-sized telescopes located around the globe (https://www.europlanet-society.org/europlanet-2024-ri/telescope-network/).</p> <p>The EPN-TN currently comprises 16 observatories with 46 telescopes ranging from 40 cm to 2 m in size. The network can be accessed free of charge to carry out projects on a wide variety of scientific studies about the Solar System and exoplanets, as well as related astronomical investigations. The network is open for new infrastructures.</p> <p> The first scientific results achieved with EPN-TN were  presented at the Europlanet Telescope Network Science Meeting held on the 6-11 February, 2022. Among 210 participants from 43 countries, there were 80 amateur astronomers participating  (http://mao.tfai.vu.lt/europlanet2022/). The network aims to strengthen collaborations between professional and amateur astronomers, who are playing an increasingly important role in planetary research. Observing time applicants from amateur astronomers are very welcome. </p> <p>We will overview the EPN-TN and its potential in fostering the collaboration between professional and amateur astronomers.</p> <p><br /><strong>Acknowledgements</strong></p> <p>Europlanet 2024 RI has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871149.</p>

Altimeter‐equipped satellites flew over the propagating area of the Indian Ocean tsunami caused by the 2004 Sumatra‐Andaman earthquake on 26 December 2004. Tsunami signals can be detected as differences in sea level changes of multiple tracks. However, observed changes in sea level differences involve not only tsunami signals but also effects from various ocean phenomena and errors due to observation technology and data processing. A multisatellite time‐spatial interpolation method is performed to define reference sea surface heights. After applying the method to the products on sea level anomalies along tracks of altimeter‐equipped satellites, quality tsunami‐height profiles with only 4‐ to 5‐cm root mean square errors were obtained for the Indian Ocean tsunami along five tracks from four satellites (Jason‐1, TOPEX/POSEIDON, ENVISAT, and Geosat Follow‐On). Maximum tsunami height in the open ocean for the 2004 Indian Ocean tsunami as observed from satellite altimetry was 1.1 m trough‐to‐crest 115 min after the main shock. Reliable tsunami‐height profiles from satellite altimetry were extracted for the first time. The method employed in this study has the potential to extract tsunami signals of 0.1 m or greater trough‐to‐crest height from satellite altimetry observation data on the deep sea by ongoing satellite missions.

This work is aimed at using the marine data of the Shared Use Centre (SUC) “IKI-Monitoring” in the variational assimilation procedures of the Informational Computational System (ICS) “INM RAS - Black Sea”. SUC “IKI - Monitoring” is a tool for obtaining remote sensing observations on the Earth state. In the paper observation data information is given, data processing procedures are described, algorithms for the assimilation of the information received and several specific features of the numerical model used are presented. Results of the variational assimilation of two sets of observation data are presented and discussed. Numerical experiments have confirmed the possibility of using incomplete data from satellites in the problems of modelling the sea area.

Abstract. GNSS reference station network is the core infrastructure for the establishment ,maintenance, renewal and service of national geodetic reference framework. At present ,nearly 3000 reference stations have been constructed in the national resources system ,of which about 2500 stations have the continuous observation capacity of BDS signal. In the face of daily massive observation data, the traditional sub-network and fully combined baselines can not meet the needs of rapid data processing for the maintenance of geodetic coordinate framework.In this paper, the high-precision and fast processing of BDS observation data of large-scale GNSS reference station network (≥ 300 stations) is realized through modular design, non difference model, parameter management to be estimated and flexible ambiguity processing. Through the whole network calculation of the 7-day BDS observation data of about 500 MGEX stations and national reference stations that selected all around the world from 211 to 217 days in 2017, the positioning results show that the average RMS values of the coordinates in the three directions of station N,E and U are ±2.3mm, ±2.8mm and ±4.6mm respectively. The orbit determination results show that the average accuracy of GPS satellite orbit in three directions is better than ±2cm; BDS MEO / IGSO track accuracy is better than ±20cm and GEO track accuracy is better than ±200cm.

Over the past decades, sensor networks have been deployed around the world to monitor over time and space a large number of properties appertaining to various environmental phenomena. A popular example is the monitoring of particulate matter and gases in ambient air undertaken, for instance, to assess air quality and inform decision makers and the public. Such infrastructure can generate large amounts of data, which must be processed to derive useful information. Infrastructure may be for environmental research, specifically. In order to reduce duplication and improve interoperability, efforts have been initiated more recently that aim at abstract architectural descriptions of infrastructure that supports the acquisition, curation, access, and processing of measurement and observation data. The ENVRI Reference Model (ENVRI-RM) is an example for an abstract architectural description of infrastructure tailored for environmental research. We briefly summarize ENVRI-RM and provide an overview of its subsystems, functionality, and viewpoints. We highlight that its primary focus is on the data life-cycle in environmental research infrastructure. As our contribution, weextend ENVRI-RM with functionality for the acquisition of knowledge from data, and the curation, access, and processing of knowledge. The extension, which we name +K, aims at addressing the knowledge life-cycle in environmental research infrastructure. We present the +K subsystems and functionality, and discuss the extension from ENVRI-RM viewpoints. We argue that the +K extension can be superimposed on ENVRI-RM to form the ENVRI-RM+K model for the ‘archetypical’ knowledge-based environmental research infrastructure that addresses both data and knowledge life-cycles. We demonstrate the application of the extension to a concrete use case in aerosol science.

Under background of rapid development of economic globalization and network information technology, rapid acquisition, effective processing and efficient use of earth observation data becomes the common requirements all over the world. Facing the challenges of massive geospatial information flow gathered from satellite remote sensing to ground observation, the paper proposes a technical framework of satellite observation data integration system. Common metadata model is established for standardized satellite data integration to solve the issue of complex satellite data directory, which can integrate multisource and heterogeneous data, and also data from different archiving systems. Through cognitive and knowledge discovery method, satellite knowledge base is built to reflect spatio-temporal changes and their association, and remote sensing big data analysis is developed for massive satellite data. Based on shared knowledge base, collaboration technology of multiple data center can be applied to address data storage and sharing of distributed massive satellite data, so that a distributed high-performance satellite data-cloud platform can be established that allows users access valuable data and information directly from different nodes. The proposed framework can provide an effective solution to distributed storage, data format conversion and interoperability for satellite remote sensing big data. Keywords-heterogeneous data integration; remote sensing big data analysis; satellite data warehouse; satellite data-cloud platform; satellite observation data integration system