Leveraging Machine Learning for Photometric Redshift Estimation of JWST Galaxies
DOI:
https://doi.org/10.2218/esjs.9996Keywords:
Astrophysics, JWST, Machine Learning, Galaxies, RedshiftAbstract
With the launch of JWST, the volume and complexity of astronomical data are increasing, a trend that will continue with future instruments such as SKA and Euclid. It is inevitable that data-driven methods will become more prominent alongside model-driven analysis. This research utilises machine learning, specifically a kernelised local linear regression model, in photometric redshift predictions of galaxies observed with JWST. With a spectroscopic dataset of 4605 galaxies, we trained the model and achieved a deviation of σdz = 0.018 and a catastrophic outlier rate of foutlier = 3.5%. These results demonstrate high accuracy and computational efficiency, highlighting the potential of machine learning for astronomical data analysis, in particular for large-scale surveys.
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Copyright (c) 2024 Julie Kalná, Ryan Begley, Callum Donnan
This work is licensed under a Creative Commons Attribution 4.0 International License.
