  In this study, we investigated the orientation model of Broad Absorption Line
(BAL) quasars using a sample of sources that are common in Sloan Digital Sky
Survey (SDSS) Data Release (DR)-16 quasar catalog and Very Large Array
(VLA)-Faint Images of the Radio Sky at Twenty Centimeters (FIRST) survey. Using
the radio cut-out images from the FIRST survey, we first designed a deep
learning model using convolutional neural networks (CNN) to classify the quasar
radio morphologies into the core-only, young jet, single lobe, or triples.
These radio morphologies are further sub-classified into core-dominated and
lobe-dominated sources. The CNN models can classify the sources with a high
precision of >98% for all the morphological sub-classes. The average BAL
fraction in the resolved core, core-dominated, and lobe-dominated quasars are
consistent with the BAL fraction inferred from radio and infra-red surveys. We
also present the distribution of BAL quasars as a function of quasar
orientation by using the radio core-dominance as an orientation indicator. A
similar analysis is performed for HiBALs, LoBALs, and FeLoBALs. All the radio
morphological sub-classes and BAL sub-classes show an increase in BAL fraction
at high orientation angles of the jets with respect to the line of sight. Our
analysis suggests that BAL quasars are more likely to be found in viewing
angles close to the equatorial plane of the quasar. However, a pure orientation
model is inadequate, and a combination of orientation and evolution is probably
the best way to explain the complete BAL phenomena.
