Related papers: A versatile classification tool for galactic activ…

[abridged] The properties of galaxies with the reddest observed R-K colors (Extremely Red Objects, EROs), including their apparent division into passive and obscured active objects with roughly similar number densities, are a known…

Machine learning (ML) algorithms become increasingly important in the analysis of astronomical data. However, since most ML algorithms are not designed to take data uncertainties into account, ML based studies are mostly restricted to data…

Classification of functional data where observations are curves or trajectories poses unique challenges, particularly under severe class imbalance. Traditional Random Forest algorithms, while robust for tabular data, often fail to capture…

Few galaxies have been found between the redshift ranges z < ~1 probed by magnitude-limited surveys and z > ~3 probed by Lyman-break surveys. Comparison of galaxy samples at lower and higher redshift suggests that large numbers of stars…

In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of about 25,000 galaxies from the second data…

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy…

We present the first results of a program to identify so far unknown active nuclei (AGN) in galaxies. Candidate galactic nuclei have been selected for optical spectroscopy from a cross-correlation of the ROSAT All Sky Survey (RASS) bright…

Using a simple two-color selection based on $g$-, $z$-, and $K$-band photometry, we pick out 1609 star-forming galaxies (sgzKs) and 422 passively evolving galaxies (pgzKs) at z\sim2$ from a $K$-band-selected sample ($K_{\rm AB} < 22.0$) in…

We have conducted a new spectroscopic survey to characterize the nature of nuclear activity in Hickson Compact Groups (HCGs) galaxies and establish its frequency. We have obtained new intermediate resolution optical spectroscopy for 200…

We characterize the incidence of active galactic nuclei (AGNs) is 0.3 < z < 1 star-forming galaxies by applying multi-wavelength AGN diagnostics (X-ray, optical, mid-infrared, radio) to a sample of galaxies selected at 70-micron from the…

This study explores the space density and properties of active galaxies to z=0.8. We have investigated the frequency and nature of unresolved nuclei in galaxies at moderate redshift as indicators of nuclear activity such as Active Galactic…

We identify a population of 640 obscured and 839 unobscured AGNs at redshifts 0.7<z<~3 using multiwavelength observations of the 9 deg^2 NOAO Deep Wide-Field Survey (NDWFS) region in Bootes. We select AGNs on the basis of Spitzer IRAC…

The presence of a large number of bots on social media leads to adverse effects. Although Random forest algorithm is widely used in bot detection and can significantly enhance the performance of weak classifiers, it cannot utilize the…

We present a new determination of the optical luminosity function (OLF) of active galactic nuclei (AGN) at low redshifts (z<0.15) based on Hubble Space Telescope (HST) observations of X-ray-selected AGN. The HST observations have allowed us…

We present GASPHOT, a tool for automated surface photometry and morphological classification of galaxies in deep and wide fields. The requirements for any such tool are reviewed, and its use for the classification of high-z galaxies is…

The classification of the optical spectra of active galactic nuclei (AGN) into different types is well founded on AGN physics, but it involves some degree of human oversight and cannot be reliably scaled to large data sets. Machine learning…

We have investigated a number of factors that can have significant impacts on the classification performance of $\gamma$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of…

In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of…

With the advent of several galaxy surveys targeting star-forming galaxies, it is important to have models capable of interpreting their spatial distribution in terms of astrophysical and cosmological parameters. To address this need, we…

We use bulge-type classifications of 809 representative SDSS galaxies by Gadotti (2009) to classify a large sample of galaxies into real bulges (classical or elliptical) and pseudobulges using Random Forest. We use structural and stellar…