Machine learning has rose to become an important research tool in the past decade, its application has been expanded to almost if not all disciplines known to mankind. Particularly, the use of machine learning in astrophysics research had a humble beginning in the early 1980s, it has rose and become widely used in many sub-fields today, driven by the vast availability of free astronomical data online. In this short review, we narrow our discussion to a single topic in astrophysics – the estimation of photometric redshifts of galaxies and quasars, where we discuss its background, significance, and how machine learning has been used to improve its estimation methods in the past 20 years. We also show examples of some recent machine learning photometric redshift work done in Malaysia, affirming that machine learning is a viable and easy way a developing nation can contribute towards general research in astronomy and astrophysics.
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28 June 2023
FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & DATA ANALYTICS: Incorporating the 1st South-East Asia Workshop on Computational Physics and Data Analytics (CPDAS 2021)
21–24 November 2021
Kuala Lumpur, Malaysia
Research Article|
June 28 2023
Machine learning applications in astrophysics: Photometric redshift estimation Available to Purchase
John Y. H. Soo;
John Y. H. Soo
a)
School of Physics, Universiti Sains Malaysia
, 11800 USM, Pulau Pinang, Malaysia
a)Corresponding author: [email protected]
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Ishaq Yahya Khalfan Al Shuaili;
Ishaq Yahya Khalfan Al Shuaili
School of Physics, Universiti Sains Malaysia
, 11800 USM, Pulau Pinang, Malaysia
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Imdad Mahmud Pathi
Imdad Mahmud Pathi
School of Physics, Universiti Sains Malaysia
, 11800 USM, Pulau Pinang, Malaysia
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John Y. H. Soo
a)
Ishaq Yahya Khalfan Al Shuaili
Imdad Mahmud Pathi
School of Physics, Universiti Sains Malaysia
, 11800 USM, Pulau Pinang, Malaysia
a)Corresponding author: [email protected]
AIP Conf. Proc. 2756, 040001 (2023)
Citation
John Y. H. Soo, Ishaq Yahya Khalfan Al Shuaili, Imdad Mahmud Pathi; Machine learning applications in astrophysics: Photometric redshift estimation. AIP Conf. Proc. 28 June 2023; 2756 (1): 040001. https://doi.org/10.1063/5.0140152
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