Automatic identification of outliers in Hubble Space Telescope galaxy images

Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical databases in the information era it can be assumed that very many outlier galaxies are included in existing and future astronomical databases. However, manual search for these objects is impractical due to the required labor, and therefore the ability to detect such objects largely depends on computer algorithms. This paper describes an unsupervised machine learning algorithm for automatic detection of outlier galaxy images, and its application to several Hubble Space Telescope fields. The algorithm does not require training, and therefore is not dependent on the preparation of clean training sets. The application of the algorithm to a large collection of galaxies detected a variety of outlier galaxy images. The algorithm is not perfect in the sense that not all objects detected by the algorithm are indeed considered outliers, but it reduces the dataset by two orders of magnitude to allow practical manual identification. The catalogue contains 147 objects that would be very difficult to identify without using automation.

Big Data in Astronomy: Surveys, Catalogs, Databases and Archives

We present the modern situation in astronomy, where Big Data coming from the Universe put new tasks for catalogizing, storage, archiving, analysis and usage of the scientific information. The two major characteristics of modern astronomy are multiwavelength (MW) studies (from γ-ray to radio, as well as multi-messenger studies, using also neutrinos, gravitational waves, etc.) and Big Data (including data acquisition, storage and analysis). Present astronomical databases and archives contain billions of objects observed in various wavelengths, both Galactic and extragalactic, and the vast amount of data on them allows new studies and discoveries. Astronomers deal with big numbers. Surveys are the main source for discovery of astronomical objects and accumulation of observational data for further analysis, interpretation, and achieving scientific results. We review the main characteristics of astronomical surveys, we compare photographic and digital eras of astronomical studies (including the development of wide-field observations), we give the present state of MW surveys, and we discuss the Big Data in astronomy and related topics of Virtual Observatories and Computational Astrophysics. The review includes many numbers and data that can be compared to have a possibly overall understanding on the studied Universe, cosmic numbers and their relationship to modern computational possibilities.

The role of Big Data in Astronomy Education

We review Big Data in Astronomy and its role in Astronomy Education. At present all-sky and large-area astronomical surveys and their catalogued data span over the whole range of electromagnetic spectrum, from gamma-ray to radio, as well as most important surveys giving optical images, proper motions, variability and spectroscopic data. Most important astronomical databases and archives are presented as well. They are powerful sources for many-sided efficient research using the Virtual Observatory (VO) environment. It is shown that using and analysis of Big Data accumulated in astronomy lead to many new discoveries. Using these data gives a significant advantage for Astronomy Education due to its attractiveness and due to big interest of young generation to computer science and technologies. The Computer Science itself benefits from data coming from the Universe and a new interdisciplinary science Astroinformatics has been created to manage these data.

IAU South West Asian ROAD

Armenia is hosting the IAU South West Asian (SWA) Regional Office of Astronomy for Development (ROAD). It is a county of ancient astronomy and is also rich in modern astronomical facilities and infrastructures, hence may successfully serve as a regional center for various activities. Byurakan Astrophysical Observatory (BAO) has 2.6m and 1m Schmidt, as well as a number of smaller telescopes that are an observational basis for joint projects and collaborations. Armenian Virtual Observatory (ArVO) is hosting astronomical databases, such as the Digitized First Byurakan Survey (DFBS) and may also serve as a basis for development of VO structures in this region. Recently we have conducted a number of new activities; a meeting on “Relation of Astronomy to other Sciences, Culture and Society" (RASCS) was organized by BAO and Armenian Astronomical Society (ArAS) in Oct 2014 in Byurakan. Activities related to Archaeoastronomy and Astronomy in Culture (AAC) were initiated as well. Discussions on future Armenian-Iranian collaboration in astronomy were carried out, including an Armenian-Iranian Astronomical Workshop held in Oct 2015 in Byurakan. Similar workshops have been carried out between BAO and Abastumani Astronomical Observatory (AbAO, Georgia) since 1974.