Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology
arXiv:2107.03589 · doi:10.1063/5.0037058
Abstract
In this work, we studied the impact of galaxy morphology on photometric redshift (photo-) probability density functions (PDFs). By including galaxy morphological parameters like the radius, axis-ratio, surface brightness and the Sérsic index in addition to the broadbands as input parameters, we used the machine learning photo- algorithm ANNz2 to train and test on galaxies from the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey. Metrics like the continuous ranked probability score (CRPS), probability integral transform (PIT), Bayesian odds parameter, and even the width and height of the PDFs were evaluated, and the results were compared when different number of input parameters were used during the training process. We find improvements in the CRPS and width of the PDFs when galaxy morphology has been added to the training, and the improvement is larger especially when the number of broadband magnitudes are lacking.
arXiv comment4 pages, 1 figure, published in the Proceedings of the 14th Asia-Pacific Physics Conference (APPC) held in Kuching, Malaysia on 17-22 November 2019
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