Spectroscopic Quasar Anomaly Detection (SQuAD) I: Rest-Frame UV Spectra from SDSS DR16
Arihant Tiwari, M. Vivek (Indian Institute of Astrophysics)

TL;DR
This paper applies anomaly detection algorithms to SDSS DR16 quasar spectra, identifying 1,888 anomalous quasars with diverse spectral features and categorizing them into multiple groups based on emission lines, metallicity, and reddening.
Contribution
It introduces a novel application of PCA and hierarchical K-Means clustering to identify and categorize quasar anomalies in a large spectroscopic dataset, including BAL and reddening features.
Findings
Identified 1,888 anomalous quasars in SDSS DR16.
Categorized anomalies into 10 broad groups based on spectral features.
Provided a value-added catalog of detected anomalies.
Abstract
We present the results of applying anomaly detection algorithms to a quasar spectroscopic sub-sample from the SDSS DR16 Quasar Catalog, covering the redshift range 1.88 < z < 2.47. Principal Component Analysis (PCA) was employed for dimensionality reduction of the quasar spectra, followed by hierarchical K-Means clustering in a 20-dimensional PCA eigenvector hyperspace. To prevent broad absorption line (BAL) quasars from being identified as the primary anomaly group, we conducted the analysis with and without them, comparing both datasets for a clearer identification of other anomalous quasar types. We identified 1,888 anomalous quasars, categorized into 10 broad groups. The anomalous groups include C IV Peakers-quasars with extremely strong and narrow C IV emission lines; Excess Si IV emitters-quasars where the Si IV line is as strong as the C IV line; and Si IV Deficient anomalies,…
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