Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies-Reference-Cited by-同舟云学术

Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies

Published:2022 Issue: Volume: Page:385-404
ISSN:1865-0929
Container-title:Artificial Intelligence and Speech Technology
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Author:

Agarwal Nidhi^ORCID,Jain Amita^ORCID,Gupta Ayush,Tayal Devendra Kumar^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95711-7_33

Reference14 articles.

1. National Aeronautics and Space Administration: Kepler and K2. https://www.nasa.gov/mission_pages/kepler/main/index.htm

2. PHL HEC (Habitable Exoplanets Catalog). http://phl.upr.edu/projects/habitableexoplanets-catalog/data/database

3. Saha, S., Bora, K., Agrawal, S., Routh, S., Narasimhamurthy, A.: ASTROMLSKIT: a new statistical machine learning toolkit: a platform for data analytics in astronomy (2015). 1504.07865

4. Advances in Intelligent Systems and Computing;K Hora,2018

5. Kok, M., Velazco J., Bentum, M.: CubeSat array for detection of RF emissions from exoplanets using inter-satellite optical communicators. In: IEEE Aerospace Conference, pp. 1–12 (2020). https://doi.org/10.1109/AERO47225.2020.9172296

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