Astronomical Surveys and Big Data

Recent all-sky and large-area astronomical surveys and their catalogued data over the whole range of electromagnetic spectrum are reviewed, from Gamma-ray to radio, such as Fermi-GLAST and INTEGRAL in Gamma-ray, ROSAT, XMM and Chandra in X-ray, GALEX in UV, SDSS and several POSS I and II based catalogues (APM, MAPS, USNO, GSC) in optical range, 2MASS in NIR, WISE and AKARI IRC in MIR, IRAS and AKARI FIS in FIR, NVSS and FIRST in radio and many others, as well as most important surveys giving optical images (DSS I and II, SDSS, etc.), proper motions (Tycho, USNO, Gaia), variability (GCVS, NSVS, ASAS, Catalina, Pan-STARRS) and spectroscopic data (FBS, SBS, Case, HQS, HES, SDSS, CALIFA, GAMA). An overall understanding of the coverage along the whole wavelength range and comparisons between various surveys are given: galaxy redshift surveys, QSO/AGN, radio, Galactic structure, and Dark Energy surveys. Astronomy has entered the Big Data era. Astrophysical Virtual Observatories and Computational Astrophysics play an important role in using and analysis of big data for new discoveries.Comment: 14 pages, 6 figures, 3 tables, 51 references. Presented at EAAS XII General Meeting, submitted to Baltic Astronom

Physics and Earth Science User Communities of Armenian National Grid Initiative

The main purpose of this article is to present the results and activities of physics and earth sciences heavy user communities of Armenian National Grid Initiative (ArmNGI) using computational or storage resources of Armenian National Grid infrastructure (ArmGrid)

ASTRONOMICAL PLATES SPECTRA EXTRACTION OBJECTIVES AND POSSIBLE SOLUTIONS IMPLEMENTED ON DIGITIZED FIRST BYURAKAN SURVEY (DFBS) IMAGES

Astronomical images extraction process with usage of the Source Extractor (SE) tool is presented in this paper. The specificity of DFBS plates is that objects are presented in low-dispersion spectral form. It does not allow extraction tools to detect the objects exact coordinates and there is need of coordinates' correction. Apart thi

Cloud-Based Machine Learning Service for Astronomical Sub-Object Classification: Case Study On the First Byurakan Survey Spectra

The classification of astronomical objects in the Digitized First Byurakan Survey (DFBS), comprising low-dispersion spectra for approximately twenty million objects, presents challenges regarding performance and computational resources. However, considering the distinct spectral characteristics within subgroups, sub-object classification becomes crucial for a more detailed understanding of the dataset. The article addresses these challenges by proposing a comprehensive cloud-based service for classifying objects into spectral classes and subtypes, with a focus on carbon stars, white dwarfs / subdwarfs, and Markarian (UV-excess) galaxies, which are the primary objects in DFBS. By leveraging the power of cloud computing, it effectively handles the computational requirements associated with analyzing the extensive DFBS dataset. The service employs advanced machine learning algorithms trained on labeled data to classify objects into their respective spectral types and subtypes. The service can be accessed and utilized through a user-friendly interface, making it accessible to a wide range of users in the astronomical community

The DFBS Spectroscopic Database and the Armenian Virtual Observatory

The Digitized First Byurakan Survey (DFBS) is the digitized version of the famous Markarian Survey. It is the largest low-dispersion spectroscopic survey of the sky, covering 17,000 square degrees at galactic latitudes |b|>15. DFBS provides images and extracted spectra for all objects present in the FBS plates. Programs were developed to compute astrometric solution, extract spectra, and apply wavelength and photometric calibration for objects. A DFBS database and catalog has been assembled containing data for nearly 20,000,000 objects. A classification scheme for the DFBS spectra is being developed. The Armenian Virtual Observatory is based on the DFBS database and other large-area surveys and catalogue data