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In this work, we studied the impact of galaxy morphology on photometricredshift (photo-$z$) probability density functions (PDFs). By including galaxymorphological parameters like the radius, axis-ratio, surface brightness andthe 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 ongalaxies from the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey.Metrics like the continuous ranked probability score (CRPS), probabilityintegral transform (PIT), Bayesian odds parameter, and even the width andheight of the PDFs were evaluated, and the results were compared when differentnumber of input parameters were used during the training process. We findimprovements in the CRPS and width of the PDFs when galaxy morphology has beenadded to the training, and the improvement is larger especially when the numberof broadband magnitudes are lacking.

Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology