  Broad Absorption Line Quasars (BALQSOs) displaying distinct blue-shifted
broad absorption lines. These serve as invaluable probes for unraveling the
intricate structure and evolution of quasars, shedding light on the profound
influence exerted by supermassive black holes on galaxy formation. The
proliferation of large-scale spectroscopic surveys such as LAMOST, SDSS, and
DESI has exponentially expanded the repository of quasar spectra at our
disposal. In this study, we present an innovative approach to streamline the
identification of BALQSOs, leveraging the power of dimensionality reduction and
machine learning algorithms. Our dataset is curated from the SDSS DR16,
amalgamating quasar spectra with classification labels sourced from the DR16Q
quasar catalog. We employ a diverse array of dimensionality reduction
techniques, including Principal Component Analysis (PCA), t-Distributed
Stochastic Neighbor Embedding (t-SNE), Locally Linear Embedding (LLE), and
Isometric Mapping (ISOMAP), to distill the essence of the original spectral
data. The resultant low-dimensional representations serve as inputs for a suite
of machine learning classifiers, including XGBoost and Random Forest models.
Through experimentation, we unveil PCA as the most effective dimensionality
reduction methodology, adeptly navigating the intricate balance between
dimensionality reduction and preservation of vital spectral information.
Notably, the synergistic fusion of PCA with the XGBoost classifier emerges as
the pinnacle of efficacy in the BALQSO classification endeavor, boasting
impressive accuracy rates of 97.60% by 10-cross validation and 96.92% on the
outer test sample. This study not only introduces a novel machine
learning-based paradigm for quasar classification but also offers invaluable
insights transferrable to a myriad of spectral classification challenges
pervasive in the realm of astronomy.
