Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Spectroscopic quasar anomaly detection (SQuAD)

View through CrossRef
Aims. We present the results of applying anomaly detection algorithms to a quasar spectroscopic subsample from the SDSS DR16 quasar catalog, covering the redshift range of 1.88 ≤ z ≤ 2.47. Methods. A principal component analysis (PCA) was employed for the dimensionality reduction of the quasar spectra, followed by a 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 separate analyses on BAL and non-BAL quasars (a.k.a. QSOs), comparing both classes for a clearer identification of other anomalous quasar types. Results. We identified 2066 anomalous quasars, categorized into 10 broadly defined 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: which exhibit significantly weaker Si IV emission compared to typical quasars. The anomalous nature of these quasars is attributed to lower Eddington ratios for C IV peakers, supersolar metallicity for Excess Si IV emitters, and subsolar metallicity for Si IV deficient anomalies. Additionally, we identified four groups of BAL anomalies: blue BALs, flat BALs, reddened BALs, and FeLoBALs, distinguished primarily by the strength of reddening in these sources. Furthermore, among the non-BAL quasars, we identified three types of reddened anomaly groups classified as heavily reddened, moderately reddened, and plateau-shaped spectrum quasars, each exhibiting varying degrees of reddening. We present the detected anomalies as an accompanying value-added catalog.
Title: Spectroscopic quasar anomaly detection (SQuAD)
Description:
Aims.
We present the results of applying anomaly detection algorithms to a quasar spectroscopic subsample from the SDSS DR16 quasar catalog, covering the redshift range of 1.
88 ≤ z ≤ 2.
47.
Methods.
A principal component analysis (PCA) was employed for the dimensionality reduction of the quasar spectra, followed by a 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 separate analyses on BAL and non-BAL quasars (a.
k.
a.
QSOs), comparing both classes for a clearer identification of other anomalous quasar types.
Results.
We identified 2066 anomalous quasars, categorized into 10 broadly defined 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: which exhibit significantly weaker Si IV emission compared to typical quasars.
The anomalous nature of these quasars is attributed to lower Eddington ratios for C IV peakers, supersolar metallicity for Excess Si IV emitters, and subsolar metallicity for Si IV deficient anomalies.
Additionally, we identified four groups of BAL anomalies: blue BALs, flat BALs, reddened BALs, and FeLoBALs, distinguished primarily by the strength of reddening in these sources.
Furthermore, among the non-BAL quasars, we identified three types of reddened anomaly groups classified as heavily reddened, moderately reddened, and plateau-shaped spectrum quasars, each exhibiting varying degrees of reddening.
We present the detected anomalies as an accompanying value-added catalog.

Related Results

Meningkatkan Kualitas Vertical Jump dengan Latihan Squad Jump
Meningkatkan Kualitas Vertical Jump dengan Latihan Squad Jump
Effective physical exercises, such as squad jumps, are crucial for enhancing athletes' performance across various sports. Squad jump training focuses on developing the leg muscle s...
Strategi Penyiaran Radio Mustang 88 FM dalam Mempertahankan Pendengar (Studi Kasus Program Mustang Morning Squad)
Strategi Penyiaran Radio Mustang 88 FM dalam Mempertahankan Pendengar (Studi Kasus Program Mustang Morning Squad)
The mass media in Indonesia have difficulty maintaining their existence during the pandemic. The media face challenges, go bankrupt or survive. Radio, as one of the mass media, is ...
Coevolution of halo and quasar properties in dense environments: CARLA J1017+6116 at z = 2.8
Coevolution of halo and quasar properties in dense environments: CARLA J1017+6116 at z = 2.8
Radio-loud active galactic nuclei, in particular radio-loud quasars, are fueled by accretion onto supermassive black holes and are among the most energetic sources in the Universe....
A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts
A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts
Industrial automation is rapidly evolving, encompassing tasks from initial assembly to final product quality inspection. Accurate anomaly detection is crucial for ensuring the reli...
The Galaxy Environment of Quasars in the z ⋍ 1.3 Clowes-Campusano Large Quasar Group
The Galaxy Environment of Quasars in the z ⋍ 1.3 Clowes-Campusano Large Quasar Group
We report significant associated clustering in the field of a z = 1.226 quasar from the Clowes-Campusano LQG in the form of both a factor ˜ 11 overdensity of I - K > 3.75 galaxi...
MedicalCLIP: Anomaly-Detection Domain Generalization with Asymmetric Constraints
MedicalCLIP: Anomaly-Detection Domain Generalization with Asymmetric Constraints
Medical data have unique specificity and professionalism, requiring substantial domain expertise for their annotation. Precise data annotation is essential for anomaly-detection ta...
Anomaly Detection in Multivariate Time Series Using Uncertainty Estimation
Anomaly Detection in Multivariate Time Series Using Uncertainty Estimation
Abstract Today’s industrial machines are equipped with several sensors that detect environmental changes and generate time series. One challenging task is the detection o...
Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
In network edge computing scenarios, close monitoring of network data and anomaly detection is critical for Internet services. Although a variety of anomaly detectors have been pro...

Back to Top