AVU-GSR is a pipeline designed to solve the problem of the Global Astrometric Sphere Reconstruction of the Gaia ESA mission whose goal is to replicate the AGIS baseline process. The pipeline produces an independent solution using a different astrometric model and different algorithms for the solution of this problem, thus providing an effective way to assess the reliability of the solution, as it is called by the absolute character of the satellite measurements. It recently passed its qualification phase with real data, successfully solving the sphere reconstruction problem at the sub-mas level with Cycle 2 data. We review the context, the current status of the pipeline, and the development needed to cope with the goal of contributing to the forthcoming Gaia Data Release four.
We GPU ported with CUDA the solver module of the Astrometric Verification Unit–Global Sphere Reconstruction (AVU–GSR) pipeline for the ESA Gaia mission. The code finds the astrometric parameters of approximately 108 sources, by solving a linear system with the LSQR. The coefficient matrix is large (10–50 TB) and sparse. The CUDA code accelerates over the original MPI + OpenMP solver of approximately 14x on CINECA cluster Marconi100. We migrated the code production to Leonardo, which has 4x GPU memory per node. This speedup was obtained without computing the system covariances, whose total number is Nunk × (Nunk − 1)/2 and occupy approximately 1 EB with Nunk approximately 5 × 108. This “Big Data” problem cannot be solved with standard methods: we defined a two jobs, I/O-based pipeline, where one job writes the files and the second concurrent job reads them, iteratively computes the covariances, and deletes them. The covariances calculation does not significantly slowdown the code until a number of covariances elements equal to approximately 8 × 106 .
The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data, supporting science in all disciplines without geographical boundaries, so that data, software, methods and publications can be shared seamlessly as part of an Open Science community. This work presents the ongoing activities related to the implementation and integration into EOSC of Visual Analytics services for astrophysics, specifically addressing challenges related to data management, mapping and structure detection. These services provide visualisation capabilities to manage the data life cycle processes under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicking and data analysis supported with machine learning techniques, for detection of structures in large scale multidimensional maps.
KEYWORDS: Telescopes, Optical instrument design, Control systems, Control systems design, Antennas, Control systems, Sensors, Interfaces, Signal processing, Receivers, Safety
The Square Kilometer Array (SKA) project aims at building the world’s largest radio observatory to observe the sky with unprecedented sensitivity and collecting area. In the first phase of the project (SKA1), an array of dishes, SKA1-MID, will be built in South Africa. It will consist of 133 15m-dishes, which will include the MeerKAT array, for the 0.350-20 GHz frequency band observations. Each antenna will be provided with a local monitor and control system (LMC), enabling operations both to the Telescope Manager remote system, and to the engineers and maintenance staff; it provides different environment for the telescope control (positioning, pointing, observational bands), metadata collection for monitoring and database storaging, operational modes and functional states management for all the telescope capabilities. In this paper we present the LMC software architecture designed for the detailed design phase (DD), where we describe functional and physical interfaces with monitored and controlled sub-elements, and highlight the data flow between each LMC modules and its sub-element controllers from one side, and Telescope Manager on the other side. We also describe the complete Product Breakdown Structure (PBS) created in order to optimize resources allocation in terms of calculus and memory, able to perform required task for each element according to the proper requirements. Among them, time response and system reliability are the most important, considering the complexity of SKA dish network and its isolated placement. Performances obtained by software implementation using TANGO framework will be discussed, matching them with technical requirements derived by SKA science drivers.
KEYWORDS: Databases, Data storage, Surface conduction electron emitter displays, Observatories, Data modeling, Galactic astronomy, Human-machine interfaces, Stars, 3D modeling, Interfaces
The VIALACTEA project has a work package dedicated to “Tools and Infrastructure" and, inside it, a task for the “Database and Virtual Observatory Infrastructure". This task aims at providing an infrastructure to store all the resources needed by the, more purposely, scientific work packages of the project itself. This infrastructure includes a combination of: storage facilities, relational databases and web services on top of them, and has taken, as a whole, the name of VIALACTEA Knowledge Base (VLKB). This contribution illustrates the current status of this VLKB. It details the set of data resources put together; describes the database that allows data discovery through VO inspired metadata maintenance; illustrates the discovery, cutout and access services built on top of the former two for the users to exploit the data content.
The core task of the Gaia mission is the solution of the Global Astrometric Sphere, which is providing the
materialization of the astrometric reference frame for the catalog that will be the main outcome of the mission. Given the absolute character of the measurements, the Gaia Data Processing and Analysis Consortium (DPAC) has decided to replicate a dedicated version of this task, together with two other ones selected for their mission criticality, in an Astrometric Verification Unit (AVU). This task, named Global Sphere Reconstruction (GSR), focusses on the importance of having an implementation of the astrometric sphere solution from a well-defined subset of objects, based on an independent astrometric model as well as on a different solution algorithm. We analyze here these two aspects in the context of the GSR implementation at the Data Processing Center of Torino (DPCT) and the solution to implement the most computationally intensive part of the pipeline as a High-Performance Computing module.
The Italian National "Galileo" Telescope (Telescopio Nazionale "Galileo" - TNG) is a 3.5m telescope located at La Palma, in the Canary islands, which has seen first light in 1998. Available TNG subsystems include four first-generation instruments, plus adaptive optics, meteo and seeing towers; the control and data handling systems are tightly coupled allowing a smooth data flow while preserving integrity. As a part of the data handling systems, the production of a local "Archive at the Telescope" (AaT) is included, and the production of database tables and hard media for the TNG Long-Term Archive (LTA) is supported. The implementation of a LTA prototype has been recently terminated, and the implementation of its operational version is being planned by the Italian National Institute for Astrophysics (INAF).
A description of the AaT and prototype LTA systems are given, including their data handling/archiving and data retrieval capabilities. A discussion of system features and lessons learned is also included, with particular reference to the issues of completeness and data quality. These issues are of particular importance in the perspective of the preparation of a national facility for the archives of data from ground-based telescopes, and its possible inclusion as a data provider in the Virtual Observatory framework.
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