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
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...
Synthesis analysis for multi-UAVs formation anomaly detection
Synthesis analysis for multi-UAVs formation anomaly detection
Purpose
The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown ...
Design and validation of a simulated multitasking environment for assessing the cognitive load on the infantry squad leader
Design and validation of a simulated multitasking environment for assessing the cognitive load on the infantry squad leader
The increasing cognitive load on infantry squad leaders is a common challenge in modern military operations. As this can increase health and safety risks, there is a need to study ...
Identitas Dakwah Perempuan dengan Techno-Religion
Identitas Dakwah Perempuan dengan Techno-Religion
The issue that is the focus of this paper is the phenomenon of identity formation that is built through da'wah conveyed by women on social media, such as the Gender Studies Communi...
The Philippines - United States - Japan - Australia Defense Diplomacy through “The Squad” : Actor, Process, and Issue
The Philippines - United States - Japan - Australia Defense Diplomacy through “The Squad” : Actor, Process, and Issue
To counter China's aggressive behavior in the Indo-Pacific, the United States, the Philippines, Japan, and Australia have formed a security partnership called “The Squad.” This stu...
Track vibration sequence anomaly detection algorithm based on LSTM
Track vibration sequence anomaly detection algorithm based on LSTM
Subway structure monitoring obtains structure monitoring data in real time, and the obtained subway track vibration sequence exhibits obvious time series characteristics. Therefore...
Anomaly Detection with Camouflage Reconnaissance in Spectral Imaging
Anomaly Detection with Camouflage Reconnaissance in Spectral Imaging
Abstract
Camouflage technology is critical in concealing targets in various environments. Traditional detection methods often rely on human visual observations , which are ...

