  Associating gamma-ray sources to their low-energy counterparts is one of the
major challenges of modern gamma-ray astronomy. In the context of the Fourth
Fermi Large Area Telescope Source Catalog (4FGL), the associations rely mainly
on parameters as apparent magnitude, integrated flux, and angular separation
between the gamma-ray source and its low-energy candidate counterpart. In this
work we propose a new use of likelihood ratio and a complementary supervised
learning technique to associate gamma-ray blazars in 4FGL, based only on
spectral parameters as gamma-ray photon index, mid-infrared colors and
radio-loudness. In the likelihood ratio approach, we crossmatch the WISE
Blazar-Like Radio-Loud Sources catalog with 4FGL and compare the resulting
candidate counterparts with the sources listed in the gamma-ray blazar locus to
compute an association probability for 1138 counterparts. In the supervised
learning approach, we train a random forest algorithm with 869 high confidence
blazar associations and 711 fake associations, and then compute an association
probability for 1311 candidate counterparts. A list with all 4FGL blazar
candidates of uncertain type associated by our method is provided to guide
future optical spectroscopic follow up observations.
