Session 7 – POSTERS

Astronomy Education is one of the major key roles in the IAU strategic plans to promote teaching and education in Astronomy across the globe. As we proceed to the digital transformation era, big data in astronomy becomes much more generic. Nowadays, the astronomical data archives are impressively large and the “digital age” has made it easy to make the data available to astronomers, researchers and even to publics. Big data in Astronomy plays an important role in Astronomy Education both in higher education and school education. Astronomers and researchers can access “big data” for the “Deep Learning” on their research works and school students and publics and also access to astronomical data and information for strengthen and promoting “Science Literacy” in the community.

We are investigating a system to assist Big Data Science, such as discovering new relationships by visualizing multivariate data using a device that displays (all-sky) like a planetarium. As a first step, we have developed a planetarium software that has a function to create a stellar HR diagram and proper motion in any direction and distance range. The data used here are mainly from the Hipparcos Catalogue, which contains about 120,000 stars, but also GAIA DR2 in part. The software can display data such as the position of the star (longitude and latitude), distance (parallax), proper motion (longitude and latitude), B-V color index, and spectral type of the star. This HR diagram generator can display color magnitude (absolute magnitude – B-V color index), apparent magnitude – B-V color index, proper motions, etc. at the same time in a pop-up window in any field of view and distance range. Using these charts, you can draw an ordinary HR diagram for understanding the evolution of stars in any field of view, and at the same time, you can visually search for groups of stars such as Open Clusters, OB associations and Moving Group.

The impressive transition from an era of scientific data scarcity to an era of overproduction has become particularly noticeable in astronomy. Today, the progress of astronomy is ensured mainly by the development of electronics and communications. In particular, the development of hardware and software, the scale of the production of increasingly powerful computers and their cheapening make them increasingly accessible to astronomers. In this way, they provide new opportunities for data collection, analysis and visualization. In this sense of development, the science of archaeoastronomy is no exception. The collection of astronomical information about prehistoric societies allows the accumulation of global data on:
– the oldest traces of astronomical activity on Earth, emotional and rational display of celestial phenomena in astronomical folklore, «folk astronomy» and timekeeping, in fine arts and architecture, in everyday life;
– the most ancient applied «astronomy» – counting the time by lunar phases, accumulation and storage of ancient databases in drawings and pictographic compositions in caves and artificially constructed objects;
– «horizon» astronomy as an initial form of observational cult astronomy, preserved only in characteristic material monuments (the oldest cult observatories) with indisputable astronomical orientations.
The report shows the importance of collecting the maximum number of artifacts and monuments from prehistory associated with the early emergence of interest in celestial phenomena. Spiritual, emotional and rational (including practical) needs that have aroused interest in Heaven are discussed. The huge variety of activities in realizing the regularity (cyclicity) of celestial phenomena as a stimulus for their use for orientation in space and time is shown. An analysis of the integrity of the primary concepts of the universe as a prerequisite for the formation of a general astronomical picture of the world (APW) long before the emergence of differentiated science and its impact on life and social experience of primitive humanity.
The character of APW in the initial stages of the development of the society is analyzed – it can be mythological, anthropomorphic, topocentric, geocentric. The role of the APW in the formation of the worldview, in the awareness of man’s connection with the cosmos, and therefore in the emergence of various religious teachings, is considered. In this regard, a modular approach in non-formal education in archaeoastronomy is proposed, related to the creation and permanent development of a specific scientific and educational environment.
In this sense, it has been shown that non-formal learning in archaeoastronomy can be linear or non-linear and often self-learning – visually or object-oriented. The results provide a basis for the formation of experience and motivation for further activities in archaeoastronomy and training for systematic work. The results of self-study – for example in astronomy, geodesy, ancient technologies and astronomy in culture – include a better understanding of the concepts, topics and evolving processes in archaeoastronomy nationally and globally. The proposed organization of non-formal education allows young researchers to gain meta-subject knowledge, to update their own research capabilities and information competencies in the Big Data era.

During the last years, the amount of data has skyrocketed. As a consequence, the data has become more expensive to store than to generate. The storage needs for astronomical data are also following this trend. Storage systems in Astronomy contain redundant copies of data such as identical files or within sub-file regions. We propose the use of Hadoop Distributed and Deduplicated File System (HD2FS) in Astronomy. HD2FS is a deduplication storage system that was created to improve data storage capacity and efficiency in distributed file systems without compromising Input/Output performance. HD2FS can be developed by modifying existing storage system environments such as the Hadoop Distributed File System. By taking advantage of deduplication technology, we can better manage the underlying redundancy of data in astronomy and reduce the space needed to store these files in the file systems, thus allowing for more capacity per volume. Keywords: Astronomy, Big Data, Deduplication, Education, File System, Hadoop, Storage System

Astronomy Education in Armenia traces back thousands of years. Numerous petroglyphs of astronomical content, ruins of the sites for astronomical observations, Stonehenge-like constructions of smaller sizes as well as astronomical terms and names used in Armenian language since II-I millennia B.C. abundantly evidence that high level astronomical knowledge had been widely exercised in Armenian plateau for thousands of years. Historical chronography mentions Anania Shirakatsi to be the most famous scientist of VII century A.D. who had been teaching astronomy, mathematics and geography.
At present Armenia accomplishes the following Astronomy Education activities, mainly by Byurakan Astrophysical Observatory (BAO) and Armenian Astronomical Society (ArAS):
• Byurakan International Summer Schools for Young Astronomers (BISS, every even year).
• Byurakan Summer Schools for Local Young Astronomers (BSS, every odd year).
• Preparing participants for International Astronomical Olympiad (annually).
• Byurakan Science Camps (BSC, annually).
• “My Universe” Essay Contests (annually).
• Galileo Teacher Training Program (GTTP), certificates for Armenian teachers.
• Teachers training by Network for Astronomy School Education (NASE, annually).
• Running Astro Clubs (annually).
• Running Junior Researchers Clubs (annually).
• Organizing School visit and lectures by professional astronomers (annually).
• Organizing visits of schools to BAO (annually) Big Data attract people to Astronomy, as many children and young people are heavily interested in computers and information technologies, and through tutorials related to Big Data Astronomy education is being successfully developed.

The primary goal of this project is to stimulate the enthusiasm of professional astronomers for astronomy education and outreach to schools and the public. At the same time, to strengthen the cooperation between professional astronomers with school teachers, policymakers, and outreach enthusiasts. In the end, we aim to prepare the students in the next generation and teachers to the challenges in the post-pandemic time and Big Data era.
We will prepare the school teachers and students with basic astronomy knowledge and a big picture of the astronomy career path, show them the process in sharing and analyzing astronomy data on a global scale. This project is a pioneering outreach project which will take place in Beijing and South-western China. It is in collaboration between IAU-NOC China and Australia, Peking University, Beijing planetarium, ICRAR (Australia), SKA, and FAST telescope. The content includes:
a) An astronomy curriculum for over 50 schools in Pingtang, China. We will together with SKA and ICRAR to design a textbook that focuses on radio astronomy introduction. At the same time, an online radio astronomy introduction courses will be provided by SKA.
b) A one-week workshop in Pingtang for students and teachers about learning basic observational astronomy skills, visiting the FAST telescope, learning the basic process of the meta-analysis of astronomy data and astronomy career path introduction. An online observatory tool will also be introduced.
c) We will also hold outreach activities dressed in astronomy development, climate change, the collaboration between astronomy researchers, and outreaches to the public in Beijing Planetarium.

The Heritage of Astronomy embraced significant developments from an interdisciplinary perspective. We discuss the contribution of Mathematics (including Indian) to the developments in Astronomy and understanding of the universe. Putting forth the limitations and challenges in pursuing an interdisciplinary research in Astronomy, we discuss the possibility and scope of Astronomical research at an institutional level. Through openly accessible databases like TESS, KEPLER, ASAS, CoRoT, GAIA, OGLE, SDSS, LAMOST so on, we emphasize the usage of software tools and the techniques of Big Data in facilitating astronomical education & research. In the light of afore mentioned views, we present the preliminary asteroseismic, photometric (using data retrieved from space based missions TESS, GAIA, KEPLER, K2) and period variation study (using archival data) and spectroscopic study (using limited ground based observations from national observational facility) of an eclipsing binary. An attempt is made to understand this binary system, its components and evolution from the obtained results drawing attention towards the impact of interdisciplinary approach in gaining better insight of the research work carried out.

In this talk we would like to present the «lessons learned» analysis of La Serena School for Data Science, a multidisciplinary program that started already in 2013. The format of the program has evolved with the years, but the basis remains the same: during 10-14 days we gather together in La Serena (Chile), a group of ~30 students either in their last years of undergrad, or the first years of grad school, with formal education in either math, statistics, physics, computer sciences, astronomy, and more recently, bio-related subjects. The students attend theoretical and hands-on sessions on different topics of Data Science and, since early on in the program, work in small multidisciplinary groups with their «mentors» in real problems of data science (motivated by either bio-science or astronomy).
Within the SOC, we have traditionally imposed strict student selection guidelines trying to maximize diversity (in addition to covering all the expenses of the school): half of the students are coming from Chilean universities and half from US institutions (with the addition of 1-3 students per year coming from Caribbean countries); we also encourage diversity of institutions (keeping a balance between students from big / well known schools and those less renowned), gender balance, and fields of expertise.
The program is very successful as proven by the very high over-subscription and the plethora of positive testimony, not only from alumni, but also from current and former mentors that keep in contact with alumni.
We believe that most of the success of the program can be grouped in two aspects: the inherent richness obtained from the multidisciplinary and diversity encouraged student (and faculty) selection process, and the strong commitment from the faculty. Regarding the latter, more than half of the professors stay through the whole school serving not only as lecturers and hands-on instructors, but also as mentors for the student group projects.
Another very relevant factor is the very strong commitment and support from AURA, NOIRLab, the LSST Corporation, NSF, REUNA, CONICYT, CORFO and other Chilean institutions that, since the very beginning understood the need to train the new generation of, not only astronomers, but scientists in data driven problems, which particularly benefit from diversity and multidisciplinarity.

In this project we use the public data from the web page “Havens-Above” to get star charts with the trajectories of the Hubble Space Telescope (HST) and the International Space Station (ISS) over the Peruvian skies from different cities in the north, center and south. These charts are used for our online space outreach activities to encourage, among other things, night sky observation and dark skies preserve trough social networks, but also astronomy education activities can be done. We will show how school children with basic knowledge of math and physics can be able to calculate rotation periods, speeds, angular velocity, acceleration due Earth´s gravity of the HST and ISS, as well as activities on spherical astronomy. We expect this kind of exercises can help teachers to share the emotion of looking the sky night to their schoolchildren.

We evaluated a new approach to the automated morphological classification of large galaxy samples based on the supervised machine learning techniques (naive bayes, random forest, support vector machine, logistic regression, and k-nearest neighbours) and deep learning using the Python programming language. As a target sample of galaxies with indeterminate morphological types, a representative sample of galaxies with ~ 315,000 SDSS DR9 objects at redshifts z < 0.1 and stellar magnitudes r <17.7 mag was considered. Classical machine learning methods were used to binary morphologically classification of galaxies into early and late types. As a result, the support vector machine gave the highest accuracy of 96.4% (early type of galaxies – 96.1%, late type of galaxies – 96.9%). Accuracy for other methods ranges from 89% to 96%. Deep machine learning methods were used to classify images of galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral). Using the neural network of the Xception architecture, it was possible to attain 94% accuracy for all classes except cigar-like galaxies (~ 88%).
These results created a basis for educational manual on the processing of large data sets for students of Ukrainian universities, which describes in detail the usage of different machine learning methods as the example of determining the morphological types of galaxies in the Python programming language.