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Journals / Türkiye Uzaktan Algılama Dergisi / 2024 / Volume: 6 Issue: 2

EXOLIFE: Detection and Habitability Estimation of Exoplanets Using Machine Learning Techniques

EXOLIFE: Makine Öğrenmesi Kullanarak Ötegezegenlerin Tespit Edilmesi ve Yaşanabilirlik Tahmini Yapılması

Eren Yılmaz0009-0001-6816-4058
Muhammet Enes Artan0009-0001-8225-5730
Ahmet Bilal Yanartaş0009-0002-0768-1198
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Publication Date
2024-12-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study successfully reached its goal of contributing important findings to the field.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Exoplanets are among the most studied and remarkable topics in astronomy.
📊
Key Findings & Data:In this context, remote sensing and machine learning, which are central to our research, have significantly accelerated the detection process by leveraging algorithms.
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Conclusion & Impact:The study successfully reached its goal of contributing important findings to the field.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Exoplanet · NASA · Machine Learning

Abstract

Exoplanets are among the most studied and remarkable topics in astronomy. Over the years, various methods have emerged for exoplanet detection, allowing for the identification of numerous exoplanet types. In this context, remote sensing and machine learning, which are central to our research, have significantly accelerated the detection process by leveraging algorithms. Our study involved training several machine learning models, including XGBoost, Random Forest, Multilayer Perceptron, K-Nearest Neighbor, Logistic Regression, and Support Vector Classifier, to compare their performance in both habitability assessment and exoplanet detection. The research utilized machine learning models trained on space observation data obtained from NASA, with the Python programming language serving as the foundation for the system's infrastructure. Our hypothesis was that "The detection of exoplanets and their evaluation within the scope of the habitability criterion can be increased to high accuracy rates with machine learning." Unlike merely detecting exoplanets, this study specifically aimed to identify Earth-like exoplanets. The XGBoost algorithm emerged as the most successful model in determining habitability, achieving an accuracy rate of 97.46% and demonstrating high precision and sensitivity. For exoplanet detection, all models achieved a main test accuracy rate of 96%; however, when considering sensitivity and precision, XGBoost was again the most effective. This research, following the synthesis and analysis of these two parameters, achieved a very high success rate compared to previous studies and made a significant contribution to the astronomy/astrophysics literature. Additionally, a Graphical User Interface (GUI) was developed, making the tested models functional through an application. The study successfully reached its goal of contributing important findings to the field.

Özet

Ötegezegenler, günümüzde astronomi alanında en çok çalışılan konular arasında yer almaktadır. Farklı türlerde oluşan ötegezegenlerin tespiti için çeşitli yöntemler geliştirilmiş ve bu sayede saptama mümkün hale gelmiştir. Bu araştırmada, ötegezegen tespiti için kullanılan uzaktan algılama ve makine öğrenmesi yöntemleri, algoritmalarla süreci hızlandırmaktadır. Projede, XGBoost, Rastgele Orman, Çok Katmanlı Algılayıcı, K-En Yakın Komşu, Lojistik Regresyon ve Destek Vektör Sınıflandırıcısı modelleri eğitilmiş ve hem yaşanılabilirlik hem de ötegezegen tespiti için karşılaştırmalar yapılmıştır. NASA verileriyle eğitilen bu makine öğrenmesi sistemi, Python yazılım diliyle oluşturulmuştur. Çalışma, “Ötegezegenlerin tespiti ve yaşanılabilirlik ölçütü kapsamında değerlendirilmesi makine öğrenmesi ile yüksek doğruluk oranlarına çıkarılabilir.” hipotezine dayanarak Dünya benzeri ötegezegenleri bulmayı hedeflemiştir. Sonuçlarda, yaşanılabilirlik saptamasında %97.46 doğruluk oranı ile XGBoost algoritması en başarılı model olarak öne çıkmıştır. Gezegen tespitinde de %96’lık doğruluk oranıyla XGBoost, en başarılı model olmuştur. Araştırma, yüksek başarı oranıyla astronomi/astrofizik literatürüne önemli katkılar sağlamıştır. Ayrıca, çalışmanın sonucunda bir Grafiksel Kullanıcı Arayüzü (GUI) oluşturulmuş ve test edilen modeller işlevsel hale getirilmiştir.

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Cite This Article

Yılmaz, E., Artan, M. E., Yanartaş, A. B. (2024). EXOLIFE: Detection and Habitability Estimation of Exoplanets Using Machine Learning Techniques. Türkiye Uzaktan Algılama Dergisi, Volume: 6 Issue: 2. https://doi.org/10.51489/tuzal.1554248

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References (37)

  1. Alei, E., Konrad, B. S., Angerhausen, D., Grenfell, J. L., Mollière, P., Quanz, S. P.,. & Wunderlich, F. (2022). Large Interferometer For Exoplanets (LIFE)-V. Diagnostic potential of a mid-infrared space interferometer for studying Earth analogs. Astronomy & Astrophysics, 665, A106. https://doi.org/10.1051/0004-6361/202243760
  2. Angerhausen, D. (2019). Big Data and Machine Learning for Exoplanets and Astrobiology: Results from NASA Frontier Development Lab. In The Tenth Moscow Solar System Symposium (pp. 244-245). https://meetingorganizer.copernicus.org/EPSC-DPS2019/EPSC-DPS2019-588-1.pdf
  3. Bapat, N. V., & Rajamani, S. (2023). Distinguishing Biotic vs. Abiotic Origins of 'Bio'signatures: Clues from Messy Prebiotic Chemistry for Detection of Life in the Universe. Life, 13(3), 766. https://doi.org/10.3390/life13030766
  4. Basak, S., Saha, S., Mathur, A., Bora, K., Makhija, S., Safonova, M., & Agrawal, S. (2020). Ceesa meets machine learning: A constant elasticity earth similarity approach to habitability and classification of exoplanets. Astronomy and Computing, 30, 100335. https://doi.org/10.1016/j.ascom.2019.100335
  5. Basant, R., Dietrich, J., & Apai, D. (2022). An Integrative Analysis of the Rich Planetary System of the Nearby Star e Eridani: Ideal Targets for Exoplanet Imaging and Biosignature Searches. The Astronomical Journal, 164(1), 12. https://doi.org/10.3847/1538-3881/ac6f58
  6. Belenkaya, E. S., Alexeev, I. I., & Blokhina, M. S. (2022). Modeling of Magnetospheres of Terrestrial Exoplanets in the Habitable Zone around G-Type Stars. Universe, 8(4), 231. https://doi.org/10.3390/universe8040231
  7. Claudi, R., & Alei, E. (2019). Biosignatures search in habitable planets. Galaxies, 7(4), 82. https://doi.org/10.3390/galaxies7040082
  8. Cuéllar, S., Granados, P., Fabregas, E., Curé, M., Vargas, H., Dormido-Canto, S., & Farias, G. (2022). Deep learning exoplanets detection by combining real and synthetic data. Plos one, 17(5), e0268199. https://doi.org/10.1371/journal.pone.0268199
  9. Dai, Z., Ni, D., Pan, L., & Zhu, Y. (2021, September). Five methods of exoplanet detection. In Journal of Physics: Conference Series (Vol. 2012, No. 1, p. 012135). IOP Publishing. https://doi.org/10.1088/1742-6596/2012/1/012135
  10. Forestano, R. T., Matchev, K. T., Matcheva, K., & Unlu, E. B. (2023). Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection. The Astrophysical Journal, 958(2), 106. https://doi.org/10.3847/1538-4357/ad0047
  11. Fujii, Y., Angerhausen, D., Deitrick, R., Domagal-Goldman, S., Grenfell, J. L., Hori, Y.,. & Stevenson, K. B. (2018). Exoplanet biosignatures: observational prospects. Astrobiology, 18(6), 739-778. https://doi.org/10.1089/ast.2017.1733
  12. Hall, C., Stancil, P. C., Terry, J. P., & Ellison, C. K. (2023). A New Definition of Exoplanet Habitability: Introducing the Photosynthetic Habitable Zone. The Astrophysical Journal Letters, 948(2), L26. https://doi.org/10.48550/arXiv.2301.13836
  13. Helled, R., Nettelmann, N., & Guillot, T. (2020). Uranus and Neptune: origin, evolution and internal structure. Space Science Reviews, 216, 1-26. https://doi.org/10.1007/s11214-020-00660-3
  14. Huang, J. (2022). Planetary Science Meets Chemistry: Studying Potential Biosignature Gases in Terrestrial Exoplanet Atmospheres (Doctoral dissertation, Massachusetts Institute of Technology). https://dspace.mit.edu/bitstream/handle/1721.1/147547/Huang-huangjc-phd-chemistry-2022-thesis.pdf?sequence=1&isAllowed=y
  15. Jagtap, R., Inamdar, U., Dere, S., Fatima, M., & Shardoor, N. B. (2021, April). Habitability of exoplanets using deep learning. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-6). IEEE. https://doi.org/10.1109/IEMTRONICS52119.2021.9422571
  16. Jara-Maldonado, M., Alarcon-Aquino, V., Rosas-Romero, R. et al. Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey. Earth Sci Inform 13, 573–600 (2020). https://doi.org/10.1007/s12145-020-00464-7
  17. Kaltenegger, L. (2017). How to characterize habitable worlds and signs of life. Annual Review of Astronomy and Astrophysics, 55, 433-485. https://doi.org/10.1146/annurev-astro-082214-122238
  18. Kong, Z., Jiang, J. H., Burn, R., Fahy, K. A., & Zhu, Z. H. (2022). Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning. The Astrophysical Journal, 929(2), 187. https://doi.org/10.3847/1538-4357/ac5c5a
  19. Krissansen-Totton, J., Thompson, M., Galloway, M. L., & Fortney, J. J. (2022). Understanding planetary context to enable life detection on exoplanets and test the Copernican principle. Nature Astronomy, 6(2), 189-198. https://doi.org/10.1038/s41550-021-01579-7
  20. Meadows, V. S., Reinhard, C. T., Arney, G. N., Parenteau, M. N., Schwieterman, E. W., Domagal-Goldman, S. D.,. & Grenfell, J. L. (2018). Exoplanet biosignatures: understanding oxygen as a biosignature in the context of its environment. Astrobiology, 18(6), 630-662. https://doi.org/10.1089/ast.2017.1727
  21. Mishra, R. (2017). Predicting habitable exoplanets from NASA's Kepler mission data using Machine Learning. Predicting habitable exoplanets from NASA's Kepler mission data using Machine Learning.
  22. Novak, R., Bradak, B., Kovacs, J., & Gomez, C. (2023). Search for Exoplanets with a Possible Surface Water Ocean. Physical Sciences Forum, 7(1), 19. https://doi.org/10.3390/ECU2023-14020
  23. Priyadarshini, I., Puri, V. A convolutional neural network (CNN) based ensemble model for exoplanet detection. Earth Sci Inform 14, 735–747 (2021). https://doi.org/10.1007/s12145-021-00579-5
  24. Ramirez, R. M. (2018). A More Comprehensive Habitable Zone for Finding Life on Other Planets. Geosciences, 8(8), 280. https://doi.org/10.3390/geosciences8080280
  25. Ranjan, S., Seager, S., Zhan, Z., Koll, D. D., Bains, W., Petkowski, J. J.,. & Lin, Z. (2022). Photochemical Runaway in Exoplanet Atmospheres: Implications for Biosignatures. The Astrophysical Journal, 930(2), 131. https://doi.org /10.3847/1538-4357/ac5749
  26. Schwieterman EW, Kiang NY, Parenteau MN, Harman CE, DasSarma S, Fisher TM, Arney GN, Hartnett HE, Reinhard CT, Olson SL, Meadows VS, Cockell CS, Walker SI, Grenfell JL, Hegde S, Rugheimer S, Hu R, Lyons TW. Exoplanet Biosignatures: A Review of Remotely Detectable Signs of Life. Astrobiology. 2018 Jun;18(6):663-708. https://doi.org/ 10.1089/ast.2017.1729.
  27. Seager, S. (2014). The future of spectroscopic life detection on exoplanets. Proceedings of the National Academy of Sciences, 111(35), 12634-12640. https://doi.org/10.1073/pnas.1304213111
  28. Seager, S., & Bains, W. (2015). The search for signs of life on exoplanets at the interface of chemistry and planetary science. Science advances, 1(2), e1500047. https://doi.org/ 10.1126/sciadv.1500047
  29. Soboczenski, F., Himes, M. D., O'Beirne, M. D., Zorzan, S., Baydin, A. G., Cobb, A. D.,. & Domagal-Goldman, S. D. (2018). Bayesian deep learning for exoplanet atmospheric retrieval. arXiv preprint arXiv:1811.03390. https://doi.org/10.48550/arXiv.1811.03390
  30. Thompson, M. A., Krissansen-Totton, J., Wogan, N., Telus, M., & Fortney, J. J. (2022). The case and context for atmospheric methane as an exoplanet biosignature. Proceedings of the National Academy of Sciences, 119(14), e2117933119. https://doi.org/10.1073/pnas.2117933119
  31. Tuchow, N. W., & Wright, J. T. (2020). A Framework for Relative Biosignature Yields from Future Direct Imaging Missions. The Astrophysical Journal, 905(2), 108. https://doi.org/10.3847/1538-4357/abc556
  32. Xin, L. (2022). Exoplanets, extraterrestrial life and beyond: an interview with Douglas Lin. National Science Review, 9(2), nwac008. https://doi.org/10.1093/nsr/nwac008
  33. Alei, E., Konrad, B. S., Angerhausen, D., Grenfell, J. L., Mollière, P., Quanz, S. P.,. & Wunderlich, F. (2022). Large Interferometer For Exoplanets (LIFE)-V. Diagnostic potential of a mid-infrared space interferometer for studying Earth analogs. Astronomy & Astrophysics, 665, A106. https://doi.org/10.1051/0004-6361/202243760
  34. Fujii, Y., Angerhausen, D., Deitrick, R., Domagal-Goldman, S., Grenfell, J. L., Hori, Y.,. & Stevenson, K. B. (2018). Exoplanet biosignatures: observational prospects. Astrobiology, 18(6), 739-778. https://doi.org/10.1089/ast.2017.1733
  35. Meadows, V. S., Reinhard, C. T., Arney, G. N., Parenteau, M. N., Schwieterman, E. W., Domagal-Goldman, S. D.,. & Grenfell, J. L. (2018). Exoplanet biosignatures: understanding oxygen as a biosignature in the context of its environment. Astrobiology, 18(6), 630-662. https://doi.org/10.1089/ast.2017.1727
  36. Ranjan, S., Seager, S., Zhan, Z., Koll, D. D., Bains, W., Petkowski, J. J.,. & Lin, Z. (2022). Photochemical Runaway in Exoplanet Atmospheres: Implications for Biosignatures. The Astrophysical Journal, 930(2), 131. https://doi.org /10.3847/1538-4357/ac5749
  37. Soboczenski, F., Himes, M. D., O'Beirne, M. D., Zorzan, S., Baydin, A. G., Cobb, A. D.,. & Domagal-Goldman, S. D. (2018). Bayesian deep learning for exoplanet atmospheric retrieval. arXiv preprint arXiv:1811.03390. https://doi.org/10.48550/arXiv.1811.03390