  [abridged] New near-infrared surveys, using the HST, offer an unprecedented
opportunity to study rest-frame optical galaxy morphologies at z>1 and to
calibrate automated morphological parameters that will play a key role in
classifying future massive datasets like EUCLID or LSST. We study automated
parameters (e.g. CAS, Gini, M20) of massive galaxies at 1<z<3, measure their
dependence on wavelength and evolution with redshift and quantify the
reliability of these parameters in discriminating between visually-determined
morphologies, using machine learning algorithms. We find that the relative
trends between morphological types observed in the low-redshift literature are
preserved at z>1: bulge-dominated systems have systematically higher
concentration and Gini coefficients and are less asymmetric and rounder than
disk-dominated galaxies. However, at z>1, galaxies are, on average, 50% more
asymmetric and have Gini and M20 values that are 10% higher and 20% lower
respectively. In bulge-dominated galaxies, morphological parameters derived
from the rest-frame UV and optical wavelengths are well correlated; however
late-type galaxies exhibit higher asymmetry and clumpiness when measured in the
rest-frame UV. We find that broad morphological classes (e.g. bulge vs. disk
dominated) can be distinguished using parameters with high (80%) purity and
completeness of 80%. In a similar vein, irregular disks and mergers can also be
distinguished from bulges and regular disks with a contamination lower than
20%. However, mergers cannot be differentiated from the irregular morphological
class using these parameters, due to increasingly asymmetry of non-interacting
late-type galaxies at z>1. Our automated procedure is applied to the CANDELS
GOODS-S field and compared with the visual classification recently released on
the same area getting similar results.
