Gözlem Hazırlık Süreçlerini Kolaylaştıracak Çok Dilli Bir Astronomi Aracı: BRK-Astronomik Araçlar

Öz

Bu çalışmada, astronomik gözlemler için zamanlama ve teleskop uygunluk değerlendirmelerini kolaylaştıran bir yazılım arayüzü geliştirilmiştir. Arayüz, gözlemcinin coğrafi konumu, teleskop özellikleri ve gök cisimlerinin hareketlerini dikkate alarak gözlem planlamasını optimize etmektedir. Python ve C# kullanılarak geliştirilen sistem, teleskop seçimi, gözlemci verisi, HJD/BJD dönüşümleri ve gözlemlenebilirlik analizlerini tek bir platformda entegre etmektedir. Teknik altyapı, Astropy, NumPy, Pandas ve Matplotlib gibi Python kütüphaneleri ile desteklenmiştir. Yazılım, SIMBAD veritabanından faydalanarak astronomik zamanlama sistemleri (JD, HJD, BJD) ile veri dönüşümlerini gerçekleştirmekte ve çok dilli destek sunmaktadır. Ayrıca, gözlem verilerinin daha verimli analiz edilmesi için veri işleme ve görselleştirme araçları entegre edilmiştir. Sonuç olarak, geliştirilen arayüz, gözlem süreçlerini hızlandırarak gözlemcilerin zamanlama ve analiz süreçlerini daha verimli yönetmelerine olanak sağlamaktadır.

Anahtar Kelimeler

• Arayüz geliştirme
• Astronomik Gözlem
• Görünürlük
• C#
• Python
• Visual Studio.

A Multilingual Astronomy Tool to Facilitate Observation Preparation Processes: BRK-Astronomical Tools

Öz

This study describes the development of a software interface that is intended to make the scheduling of astronomical observations and the assessment of telescope suitability easier. Observer's geographical location, telescope specifications, and celestial objects' motions are all taken into account by the interface to optimize observation planning. The system, which was developed using Python and C#, integrates telescope selection, observer data management, HJD/BJD conversions, and observability analyses into a single platform. Astropy, NumPy, Pandas, and Matplotlib, among other scientific Python libraries, support the technical framework. Furthermore, the software retrieves astronomical data from the SIMBAD database and performs time system conversions (JD, HJD, BJD) with multilingual support. Data processing and visualization have been added to increase efficiency and allow for more accurate observation analysis. In summary, the created interface improves the efficiency and accuracy of astronomical observations by streamlining observational workflows and enabling precise time calculations, observability evaluations, and data transformations.

Anahtar Kelimeler

• Visual Studio.
• Interface development
• Astronomical Observation
• Visibility
• C#
• Python

Kaynakça

1. Amann S., Proksch S., Nadi S., Mezini M. A study of visual studio usage in practice. 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) 2016; 124–134. https://doi.org/10.1109/SANER.2016.39
2. Beller M., Gousios G., Panichella A., Zaidman A. When, how, and why developers (do not) test in their IDEs. Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering 2015; 179–190. https://doi.org/10.1145/2786805.2786843
3. Bonnarel F., Fernique P., Bienaymé O., Egret D., Genova F., Louys M., Ochsenbein F., Wenger M., Bartlett JG. The aladin interactive sky atlas: a reference tool for identification of astronomical sources. Astron Astrophys Suppl Ser 2000; 143: 1–14. https://doi.org/10.1051/aas:2000331
4. Eastman J., Siverd R., Gaudi BS. Achieving better than 1 minute accuracy in the heliocentric and barycentric Julian dates. Publications of the Astronomical Society of the Pacific 2010; 122(894): 935–946.
5. Ginsburg A., Sipőcz BM., Brasseur CE., Cowperthwaite PS., Craig MW., Deil C., Guillochon J., Guzman G., Liedtke S., Lim PL., Lockhart KE., Mommert M., Morris BM., Norman H., Parikh M., Persson MV., Robitaille TP., Segovia JC., Singer LP., Tollerud EJ., de Val-Borro M., Valtchanov I., Woillez J. Astroquery: An astronomical web-querying package in python. Astron J 2019; 157: 98. https://doi.org/10.3847/1538-3881/AAFC33
6. Harris CR., Millman KJ., van der Walt SJ., Gommers R., Virtanen P., Cournapeau D., Wieser E., Taylor J., Berg S., Smith NJ., Kern R., Picus M., Hoyer S., van Kerkwijk MH., Brett M., Haldane A., del Río JF., Wiebe M., Peterson P., Gérard-Marchant P., Sheppard K., Reddy T., Weckesser W., Abbasi H., Gohlke C., Oliphant TE. Array programming with numpy. Nature 2020; 585: 357–362. https://doi.org/10.1038/s41586-020-2649-2
7. Hunter JD. Matplotlib: A 2D graphics environment. Comput Sci Eng 2007; 9(3): 90–95. https://doi.org/10.1109/MCSE.2007.55
8. Isaac Newton Group of Telescopes. Staralt – altitude and azimuth of stars. Accessed July 24, 2025. https://astro.ing.iac.es/staralt/

9. Jones E., Oliphant T., Peterson P. Scipy: open source scientific tools for python. 2001.
10. Kitchin CR. Astrophysical techniques. CRC Press; 2003.
11. Kutner ML. Astronomy: A physical perspective. Cambridge University Press 2003. https://doi.org/10.1017/cbo9780511802195
12. Lopez Gonzalez-Nieto P., Gomez Flechoso M., Arribas Mocoroa ME., Muñoz Martin A., Garcia Lorenzo ML., Cabrera Gomez G., Alvarez Gomez JA., Caso Fraile A., Orosco Dagan JM., Merinero Palomares R., Lahoz-Beltra R. Design and development of a virtual laboratory in python for the teaching of data analysis and mathematics in geology: Geopy. 2020; :2236–2242. https://doi.org/10.21125/inted.2020.0687
13. Lutz M. Programming python. O’Reilly Media 2010.
14. McKinney W. Data structures for statistical computing in python. 2010; 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
15. McKinney W. Python for data analysis. O’Reilly Media 2017. Ohio State University Department of Astronomy. Time utilities. Accessed July 24, 2025. https://astroutils.astronomy.osu.edu/time/
16. Oliphant TE. Python for scientific computing. Comput Sci Eng 2007; 9: 10–20. https://doi.org/10.1109/MCSE.2007.58
17. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay É. Scikit-learn: machine learning in python. Journal of Machine Learning Research 2011; 12: 2825–2830.
18. Rybicki GB., Lightman AP. Radiative processes in astrophysics. John Wiley & Sons; 1979. https://doi.org/10.1002/asna.2113070305
19. Troelsen A., Japikse P. Pro c# 9 with .net 5. Apress; 2021. https://doi.org/10.1007/978-1-4842-6939-8 Van der Walt S., Colbert SC., Varoquaux G. The numpy array: a structure for efficient numerical computation. Comput Sci Eng 2011; 13: 22–30. https://doi.org/10.1109/MCSE.2011.37.
20. Wenger M., Ochsenbein F., Egret D., Dubois P., Bonnarel F., Borde S., Genova F., Jasniewicz G., Laloë S., Lesteven S., Monier R. The simbad astronomical database. Astron Astrophys Suppl Ser 2000; 143. https://doi.org/10.1051/aas:2000332
21. Young AT. Air mass and refraction. Applied Optics 1994; 33(6): 1108–1110.