Abstract:
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 catalogue 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 lobedominated sources. The CNN models can classify the sources with a high precision of >98 per cent 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 infrared 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
