BÖLÜM


DOI :10.26650/B/ET06.2020.011.02   IUP :10.26650/B/ET06.2020.011.02    Tam Metin (PDF)

Astronomical Data

Hulusi GülseçenHasan Hüseyin Esenoğlu

Space telescopes have increased the quality of data collection for today’s astronomy. In parallel to this, obtaining high quality data with high technology and good resolution focal plane detectors in accordance with the developments in material science in the ground-based observations has been achieved. With the new generation of ground based and space observations, global campaigns also brought continuity in data acquisition and increased performance. Finally, the fact that theoretical outputs can be made to allow in today’s technology, for example, the detection of gravitational waves in the universe and these add new ones to the existing data. In addition, there has been a significant increase in data archiving, reduction and processing together with the number and variety of data collection tools. Astronomers have been able to overcome the facilitation in these processes in their own way: manpower waste has been reduced with autonomous telescopes, the data has been transformed into informatics (astroinformatics) with pipelines, the workload has been reduced to large masses by establishing a virtual observatory, and finally smart applications have been opened with the provided big data and new open areas have been reached with a future such as data mining. In this way, there has been progress in solving many astronomical events in the universe. This chapter is orginized in two subsections. In first, we are discussing how to solve problems in astronomy by using big data. In the second, we mention about big data sources in astronomy. The importance of data in astronomy, sources of data, big data in regards to the discovery of universe and analyzing data are the topics discussed in these subsections.



Referanslar

  • Brahem, M., Yeh, L., Zeitouni, K. (2018). ASTROIDE: A Unified Astronomical Big Data Processing Engine over Spark. A Preprint, October 25. google scholar
  • Dindar, M., Helhel, S., Esenoglu, H., Parmaksizoglu, M. (2015). A new software on TUG-T60 autonomous telescope for astronomical transient events. Experimental Astronomy, 39(1), 21–28 google scholar
  • Djorgovski, S., G. (2017). Astronomy in the Era of Big Data- From Virtual Observatory to Astroinformatics and beyond. TIARA Summer School on Astrostatistics and Big Data Taipei, Taiwan, September. google scholar
  • Gómez-Vargas, G. (2018). First Ideas to Connect Astronomical Data, Deep Learning and Image Analysis, Accelerating the search of dark matter with machine learning. Lorentz Center, Leiden, January. google scholar
  • Estévez, P. (2016). Big Data Era Challenges and Opportunities in Astronomy: How SOM/LVQ and Related Learning Methods Can Contribute? WSOM 2016 Houston, TX, January 8. google scholar
  • Feigelson, E. D. & Babu, G. J. (2012). Big data in astronomy. Significance, The Royal Statistical Society, August. google scholar
  • Kaynar, S. (2019). Determination of the Trajectory of Selected Several Near Earth Asteroids and Investigation Their Physical Properties. Akdeniz University Graduate School of Natural and Applied Sciences Department of Physics Master Thesis (October). google scholar
  • Kremer, J., Kristoffer, S. S., Gieseke, F., Steenstrup, K. P., Igel, C. (2017). Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy, IEEE Intelligent Systems, 32, 16–22, March–April (https:// arxiv.org/abs/1704.04650). google scholar
  • Marks, J. (2011). 5 Things You Need to Know About Big Data. NetApp. Veteran Data Solutions-VetDS google scholar
  • Mazeh, T. & Poznanski, D. (2018). Big Data and Exo-Planets, Proposal for a research group in Astronomy. google scholar
  • McEwen, J. (2016). Big-Data in Astronomy and Astrophysics Extracting Meaning from Big-Data. (https://indico.hephy.oeaw.ac.at/event/86/session/3/contribution/1/material/slides/0.pdf google scholar
  • Mehta, P., Dorkenwald, S., Zhao, D., Kaftan, T., Cheung, A., Balazinska, M., Rokem, A. (2017). Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads. Andrew Connolly, Jacob Vanderplas, Yusra AlSayyad, Proceedings of the VLDB Endowment, Vol. 10, No. 11. google scholar
  • Meyer, E. (2018). Big Data is Transforming How Astronomers Make Discoveries, The Conversation, May 15 (https://theconversation.com/the-next-big-discovery-in-astronomy-scientists-probably-found-it-years-agobut-they-dont-know-it-yet-95280?xid=PS_smithsonian) google scholar
  • Morgan, H. (2018). Large Synoptic Survey Telescope (LSST) Scaling Issues and Network Needs, Pacific Northwest Gigapop Meeting October 23. google scholar
  • Raynard, L. (2017). Radio Astronomy & SDGs A Justification or Solution? South African Radio Astronomy Observatury (SARAO), September 4. google scholar
  • Scaife, A. (2019). Big Telescope, Big Data: Towards Exa-Scale With the SKA, Numerical algorithms for highperformance computational science. Royal Society 8–9 April. google scholar
  • Scaife, A. (2016). Big Telescope, Big Data: Indirect Imaging in the SKA Era, IAU Astroinformatics, Sorrento. google scholar
  • Scholz, T. M. (2017). Big data in organizations and the role of human resource management: A complex systems theorybased conceptualization. Econstor, Personalmanagement und Organisation, No. 5, Peter Lang International Academic Publishers (http://hdl.handle.net/10419/182489) google scholar
  • Siemiginowska, A., Eadie, G., Czekala, I. with 33 authors. (2019). Astro2020 Science White Paper: The Next Decade of Astroinformatics and Astrostatistics. 15 March. (https://arxiv.org/abs/1903.06796) google scholar
  • Wyrzykowski, L. et al. (2020). Full Orbital Solution for the Binary System in the Northern Galactic Disk Microlensing Event Gaia16aye, Astronomy & Astrophysics, in pressed (633, A98.) google scholar
  • Zhang, Y., Zhao, Y. (2015). Astronomy in the Big Data Era. Data Science Journal, 14(11), 1–9 (https:// datascience.codata.org/articles/10.5334/dsj-2015-011). google scholar
  • Zhang, Z., Barbary, K., Nothaft, F. A., Sparks, E. R., Zahn, O., Franklin, M. J., Patterson, D. A., Perlmutter, S. (2016). Kira: Processing Astronomy Imagery Using Big Data Technology, IEEE Transactions on Big Data, DOI: 10.1109/TBDATA.2016.2599926. google scholar


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