  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.
