Related papers: Exploring the Spectral Space of Low Redshift QSOs

As the third paper in a series regarding the estimation of luminosity functions (LFs) via kernel density estimation (KDE), we present a further generalization of our framework by extending its applicability to multiple flux-limited samples.…

Quasars at the redshift frontier (z > 7.0) are fundamental probes of black hole (BH) growth and evolution but notoriously difficult to identify. At these redshifts, machine learning-based selection methods have proven to be efficient, but…

Gravitational wave echoes offer a unique probe of the near-horizon structure of astrophysical black holes, beyond the standard ''black hole spectroscopy''. Theoretical waveform predictions, however, remain uncertain, motivating robust…

Wide area Large-Scale Structure (LSS) surveys are planning to map a substantial fraction of the visible universe to quantify dark energy through Baryon Acoustic Oscillations (BAO). At increasing redshift, for example that probed by proposed…

Slitless spectroscopy is a traditional method for selecting quasars. In this paper, we develop a procedure for selecting quasars (QSOs) using the 3D-HST G141 slitless spectra. We initially identify over 6,000 sources with emission lines…

Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF)…

The deviations of non-linear perturbations of black holes from the linear case are important in the context of ringdown signals with large signal-to-noise ratio. To facilitate a comparison between the two we derive several results of linear…

We present a new approach to capturing the broad diversity of emission line and continuum properties in quasar spectra. We identify populations of spectrally similar quasars through pixel-level clustering on 12,968 high signal-to-noise…

Integrative analysis of multiple heterogeneous datasets has become standard practice in many research fields, especially in single-cell genomics and medical informatics. Existing approaches oftentimes suffer from limited power in capturing…

We present a detailed characterization of the 849 broad-line quasars from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project. Our quasar sample covers a redshift range of 0.1<z<4.5 and is flux-limited to i_PSF<21.7 without…

The Karhunen-Lo\`eve Expansion (KLE) of a stochastic process is a well understood eigenfunction expansion used widely in time series analysis, stochastic PDEs, and signal processing. Karhunen-Lo\`eve expansions have also been proven to…

This paper presents a study of the environments of SDSS Quasi-Stellar-Objects (QSOs) in the Spitzer Extragalactic Representative Volume Survey (SERVS). We concentrate on the high-redshift QSOs as these have not been studied in large numbers…

The increasing area and depth of galaxy surveys will give us access to the largest scales in the Universe and allow for a direct test of the primordial power spectrum set by inflation. To take full advantage of the survey's volume, we must…

We study the estimators of various second-order weak lensing statistics such as the shear correlation functions xi_\pm and the aperture mass dispersion <M_ap^2> which can directly be constructed from weak lensing shear maps. We compare the…

We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting PINK, a self-organising map algorithm, we are able to…

Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between…

We have conducted an optical/near-infrared study of the environments of radio-loud quasars (RLQs) at redshifts z=0.6-2. In this paper we discuss the sample selection and observations for the z=1-2 subsample and the reduction and cataloguing…

The deep learning model Transformer has achieved remarkable success in the hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial Self-Attention (SA) mechanisms. However, applying these designs to remote sensing (RS)…

We present a near-infrared quasar composite spectrum spanning the wavelength range 0.58 - 3.5 um. The spectrum has been constructed from observations of 27 quasars obtained at the NASA IRTF telescope and satisfying the criteria Ks < 14.5…

We introduce a novel bottom-up approach for the extraction of chart data. Our model utilizes images of charts as inputs and learns to detect keypoints (KP), which are used to reconstruct the components within the plot area. Our novelty lies…