Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology

TL;DR

This study investigates how incorporating galaxy morphology parameters into machine learning models improves the accuracy and quality of photometric redshift probability density functions, especially when broadband data is limited.

Contribution

It demonstrates that adding galaxy morphological features enhances photo-$z$ PDF quality, particularly in data-sparse scenarios, using the ANNz2 machine learning algorithm.

Findings

Improved CRPS and PDF width with morphological data inclusion.

Greater improvements observed when broadband data is limited.

Morphological parameters significantly enhance photo-$z$ PDF accuracy.

Abstract

In this work, we studied the impact of galaxy morphology on photometric redshift (photo-$z$) probability density functions (PDFs). By including galaxy morphological parameters like the radius, axis-ratio, surface brightness and the S\'ersic index in addition to the $ugriz$ broadbands as input parameters, we used the machine learning photo-$z$ 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.