This talk delivers the results of a common project between the University of Sharjah and Leicester University “Prof. Martin Barstow” a member of the Gaia team. We have recalculated the dynamical masses of a sample of 1700 close visual binary stars using Gaia DR2 and compare the results with masses derived from both the original and enhanced Hipparcos data. We consider the impact of the Gaia DR2 parallaxes on our understanding of the sample of visual binaries.

Training the students to analyze close visual binary and multiple stats. All subroutines of Al-Wardat’s method will be given for free to all participants. The following steps will be applied:

• Data collections from different catalogues.
• Applying Al-Wardat’s method to estimate the physical and geometrical parameters, estimating synthetic spectral energy distributions, calculating synthetic photometry, plotting HR, estimating masses, spectral types and ages.
• Plotting the results and comparing the estimated masses with dynamical masses.
• Orbital solutions using Tokovinin’s Code (depending on time).

Continuous FT. Odd and even extensions. Infinite and finite signals. The Parceval theorem. Discrete FT. Sampling. Kotelnikov's theorem. Shannon's sinc function as an asymptotic function. The Gibbs phenomenon. Semiamplitude, phase and power spectra. Examples of spectra for functions with or withiut junps of the function of its derivatives. Jumps as a cause of apparent excess in amplitudes and power spectra. Spectral window. Wehlau & Leung (1964). ACF and CCF. FFT and its difference from DFT. Least squares. The coefficients. Statistically significant degree of the model. Errors of the coefficients. Irregularly spaced signals. Naive usage of the oversimplified formulae. Methods of Deeming (1975), Lomb (1976), Scargle (1983), algorithms CLEAN and CLEANEST. Least squares (Andronov 1994OAP.....7...49A; MCV). Harmonic and multiharmonic approximations. The multiharmonic Aproximation with trend. The period correction. Description of the phase curve by semi-amplitudes and shifts in respect to the epoch of extremum. Phase plane diagrams and chaos. Variety of definitions of amplitude, harmonic, phase. Dynamic spectra. Wavelet analysis. Scalegram analysis. Special shapes. Running parabola and running sine. Multi-period nad multi-harmonic models with trend. Covariation matrix of the coefficients. Software MCV and MAVKA. Planning the observations, if possible. Inter-longitude Astronomy (ILA) project. An extensive review was recently published in 2020kdbd.book..191A

The Virtual Observatory (VO) is the vision that astronomical datasets and other resources should work as a seamless whole. It is also a data-intensively online astronomical research and education environment, taking advantages of advanced information technologies to achieve seamless, global access to astronomical information. Many projects and data centers worldwide are working towards this goal. The International Virtual Observatory Alliance (IVOA) was formed in June 2002 with a mission to facilitate the international coordination and collaboration necessary for the development and deployment of the tools, systems and organizational structures necessary to enable the international utilization of astronomical archives as an integrated and interoperating virtual observatory. Chinese Virtual Observatory (China-VO) is the national VO project in China initiated in 2002 by the Chinese astronomical community leading by National Astronomical Observatories, CAS. In the talk, I will give a brief introduction about the idea of VO, an overview of the IVOA and current status of the China-VO.

Lecture abstract: Low-resolution spectra like those resulting from objective prism observations or the RP/BP instrument on board of the Gaia astrometry satellite enable a wealth of interesting science – and you can develop for much of this now, since the spectra taken within the First Byurakan Survey share quite a few of their properties, and they are available right now using the Virtual Observatory (VO).

Tutorial abstract: This tutorial will start with a short introduction into what the VO is and then roughly follow the use case at http://www.g-vo.org/tutorials/dfbs.pdf: Going from simple, one-serivce discovery in interactive tools to programmatic (python-based) analysis to global discovery, all that under the common theme of carbon stars, but applicable in many fields of astrophysics.

Lecture #1 abstract: The Herschel mission, its on-board instruments, and scientific goals will be introduced. The main Herschel observing programs for the photometric study of the interstellar medium will be briefly described, with particular regard to the aspects of data acquisition, map making, and data storage. To do that, involved physical quantities, basics of coordinate projection, and technical details will be presented.

Tutorial #1 abstract: How to search the Herschel Science Archive and to download photometric maps will be illustrated, with particulate regard to the Hi-GAL survey data. We will handle the downloaded maps with the DS9, obtaining false colour images and overlapping source catalogs.

Lecture #2 abstract: The role of the dust in the interstellar medium will be illustrated, together with typical physical conditions of molecular clouds. A brief summary of the early phases of star formation will be also given. The way to obtain quantitative information about the physics of the interstellar clouds and star formation sites from Herschel photometric data will be shown.

Tutorial #2 abstract: How to obtain temperature and column density maps from Herschel photometric observations will be illustrated. The statistical distributions of these two quantities will be analyzed, highlighting differences between quiescent and star-forming clouds, and/or among different locations across the Galactic plane. Evolutionary implications will be discussed.

Machine Learning and other Artificial Intelligence (AI) techniques have for long been used in astronomy to address classification and regression problems. They provide very useful means for quickly and reliably addressing problems which would be very difficult to approach through conventional means. Astronomers are now generating vast quantities of data using ever more advanced telescopes, which make the use of such techniques inescapable. In recent years Deep Learning, which is at the heart of applications like face recognition and “driverless” cars has become a very important tool for researchers across domains. In my talk, I will describe the basics of Machine and Deep Learning and some interesting applications of these techniques to Big Data in Astronomy.

The Zwicky Transient Facility (ZTF) with its large field of view of 47 Sq. Degrees has been operational for over two years. The 1.4 TB data collected nightly is processed using state of the art pipelines and the ensuing science has been phenomenal with thousands of supernovae, and an equal number of exciting transients of other types, variables, and asteroids, including a few inside the Earth's orbit. We use deep learning during the initial phases to separate instrumental artifacts from genuine transients, satellite streaks from nearby streaking asteroids, and later to classify variables and transients. We will provide an overview of these applications, and show through simple examples the importance of data preprocessing, and the importance of understanding possible biases.

One of the main problems of astrophysics is the study of the physical properties of the surface layers of stars. Stars are observed through the interstellar dust, therefore their light is dimmed and reddened. This fact complicates the parameterization and classification of stars. Parameters of a star, as well as interstellar reddening, can be obtained from its spectrum. However, to get spectral energy distribution with good dispersion and sufficient accuracy one needs to use a large telescope unless the investigated object is bright enough. Therefore, the solution to the problem of the parameterization of stars based on their photometry is a topical issue. Recently constructed large photometric surveys as well as VO-tools for cross-matching their objects provide us with a possibility to get multicolor photometric data for millions of objects. Consequently, it allows users to parameterize objects and determine interstellar extinction in the Galaxy.

The study of properties of binary stars is an extremely important problem of astrophysics and stellar astronomy. The components of a binary star have the same age and are assumed to be born at the same place and time (i.e., act as excellent tests of theoretical models of evolution and tests of chemical abundance). They are situated at the same distance and suffer the same interstellar extinction, which is useful for distance calibrations and the study of interstellar reddening. The binary stars provide us with precise data on the absolute stellar masses and radii. Also, they give access to system characteristics (orbital parameters). Last but not least, binary stars are very numerous: at least half of the stars in the galactic neighborhood belong to double or multiple systems. Construction of catalogs/databases of binary stars has a long history. Compilations of data on binary stars (catalogs and databases) have been published for visual, interferometric, eclipsing, spectroscopic, and other types of systems. However, there was no database synthesizing the various categories. Binary star DataBase (BDB) is the database of binary/multiple systems of various observational types. BDB contains data on physical and positional parameters of 260,000 components of 120,000 stellar systems of multiplicity 2 to more than 20, taken from several dozen original catalogs and databases. The search for objects in the database is possible both with the identifier and by parameters. We describe the new features in the organization of the database, integration of new catalogues and implementation of new possibilities available to users.

The Hertzsprung–Russell diagram (HRD) is a scatter plot of stars showing the relationship between the stars' absolute magnitudes or luminosities versus their stellar classifications or effective temperatures. We will construct HRD based on VizieR and Gaia data obtained through the use of the CDS X-Match service and TOPCAT package.

We will acquire Gaia data in the stellar cluster's region to select and examine members of the cluster, determine its parallax and construct color-magnitude and other diagrams. The tutorial is based on tutorials by Mark Taylor and Nyall Dikon.

This Introductory Lecture will make acquainted the students with the prominent scientist of the 20th century and the founder of the Byurakan Astrophysical Observatory (BAO) Viktor Ambartsumian (1908-1996) and BAO itself. Ambartsumian was the IAU President in 1961-1964, ICSU President in 1968-1972, the President of the Armenian Academy of Sciences in 1947-1993 and the Director of BAO in 1946-1988. His outstanding results in the field of theoretical astrophysics, stellar astrophysics, planetary nebulae, cosmic dense matter, the activity of the galactic nuclei, as well as theoretical physics and mathematics significantly changed our understanding about the Universe. Beside Ambartsumian’s achievements, BAO is well known by its surveys (Markarian Survey, Arakelian galaxies and others) and discovery of dozens of thousands of new cosmic objects: T Tau and flare stars, HH objects, cometary nebulae, carbon stars, white dwarfs, cataclysmic variables, Novae and Supernovae, UV-excess galaxies, AGN, Starbursts, compact groups of compact galaxies (Shahbazian groups), etc. At present, BAO has official status by the Armenian Government as National Value and IAU South West and Central Asian Regional Centre of Astronomy for Development, as well as Markarian Survey has entered the UNESCO “Memory of the World” documentary heritage list.

Astronomical surveys and catalogs are the main sources for the discovery of new objects, both Galactic and extragalactic. I will review the current background in astronomy for further all-sky or large-area studies. Modern astronomy is characterized by multiwavelength (MW) studies (from γ-ray to radio) and big data (data acquisition, storage and analysis). Present astronomical databases and archives contain billions of objects observed at various wavelengths, and the vast amount of data on them allows new studies and discoveries. Surveys are the main source also for accumulation of observational data for further analysis, interpretation, and achieving scientific results. We review the main characteristics of astronomical surveys (homogeneity, completeness, sensitivity, etc.), compare photographic and digital eras of astronomical studies (including the development of wide-field observations), and describe the present state of MW surveys. Among others, Fermi-GLAST, INTEGRAL (γ-ray), ROSAT, Chandra, XMM (X-ray), GALEX (UV), DSS1/2, SDSS, Gaia (optical), 2MASS, IRAS, AKARI, WISE (IR), NVSS and FIRST (radio) surveys will be presented and discussed, as well as surveys for variable and transit objects.

One of the main structural properties of young stellar objects is the presence of circumstellar disks and envelopes. These formations consist of gas-dust matter, which forms an infrared excess. Therefore, infrared excess can be considered as one of the main characteristics of young stars. Moreover, the measure of infrared excess in the near and mid-infrared wavelengths is used to classify the evolutionary stage of stars from prestellar objects to "older" PMS objects with the Class III evolutionary stage. The lecture discusses methods for the selection and classification of young stars in star-forming regions based on their photometric data, borrowed from various infrared astronomical surveys.

The concept of "Open Data", as defined within a multi-disciplinary environment, will be discussed. The FAIR paradigm (Findable, Accessible, Interoperable, Reusable) for data will be introduced, together with the clauses to be fulfilled to guarantee FAIRness. Finally, it will be shown that the Virtual Observatory is FAIR in its concept and implementations.

In the past years, Open Science Clouds have been defined and implemented with the goal of allowing scientists, technologists, and the interested public to exploit Open Data. This goal expands the standard concept of Cloud and calls for tighter integration of data and computing resources. Adaptation of these concepts to the Astro domain will be discussed.

SODISM is a kind of camera onboard the satellite Picard in orbit around the Earth. SODISM regularly takes photos of the Sun to study its atmosphere. We will use these images obtained during the transit of Venus (passage of Venus in front of the Sun for an observer on Earth) to explore the peculiarity of the space experiments. For this we will use true scientific observations. Understand some operational parts of a space experiment for real examples. Some definitions and reminders of the scientific objectives of SODISM: PICARD is a french satellite dedicated to Solar studies, SODISM is dedicated to the measure of the solar diameter and its variations related to the SUN-Climate of the Earth's relations. SODISM is a telescope taking images of the Sun. SODISM is not the first to measure the solar diameter, but he makes it with a very high precision. The Solar diameter: is it variable during time? Is it affecting the climate of the Earth? These are the two important questions scientists want to answer. But here, we will limit our analysis to the study of a space experiment . For this, we will use real scientific results.

The accurate measurement of the solar spectrum at the top of the atmosphere and its variability are fundamental inputs for solar physics (Sun modeling), terrestrial atmospheric photochemistry and Earth’s climate (climate’s modeling). These inputs were the prime objective set in 1996 for the SOLAR mission. The objective of this tutorial is to play with this spectrum with filters and instrumental effects in order to understand the role of spectral resolution, atmospheric absorption etc… on the solar light reaching the ground/the instrument.