Related papers: How to Find Variable Active Galactic Nuclei with M…
Observations and theoretical simulations suggest that the large scale environment plays a significant role in how galaxies form and evolve and, in particular, whether and when galaxies host an actively accreting supermassive black hole in…
We report the discovery of a rare new form of long-term radio variability in the light-curves of active galaxies (AG) --- Symmetric Achromatic Variability (SAV) --- a pair of opposed and strongly skewed peaks in the radio flux density…
Accreting supermassive black holes at the centres of galaxies are the engine of active galactic nuclei (AGN). X-ray light curves of unabsorbed AGN show dramatic random variability on timescales ranging from seconds to years. The power…
The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…
Most of the variability studies of active galactic nuclei (AGNs) are based on ensemble analyses. Nevertheless, it is interesting to provide estimates of the individual variability properties of each AGN, in order to relate them with…
These lectures take a look at how observations with adaptive optics (AO) are beginning to influence our understanding of active galactic nuclei (AGN). By focussing on a few specific topics, the aim is to highlight the different ways in…
Using Spitzer-IRS spectroscopy, we investigate the ubiquity of Active Galactic Nuclei (AGN) in a complete (~94%), volume-limited sample of the most bolometrically-luminous galaxies (L_IR > (0.3-20) x 10^10 L_sun) to D < 15 Mpc. Our analyses…
The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning…
(abridged) The overwhelming majority of diagnostic tools for galactic activity are focused on active galaxies. Passive or dormant galaxies are often excluded from these diagnostics which usually employ emission line features. In this work,…
We use data from the All Wavelength Extended Groth Strip International Survey to construct stacked X-ray maps of optically bright active galaxies (AGN) and an associated control sample of galaxies at high redshift (z less than 0.6). From…
Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
Active Galactic Nuclei (AGN) are ideal sources for multi-wavelength studies as their emission can cover almost 20 orders of magnitude in frequency from the radio to the gamma-ray band. After reviewing their basic properties, I will assess…
We present a census of local active galactic nuclei (AGN) at a redshift of $z\leq0.025$ selected using the high-ionization [Ne v] $\lambda14.32\,\mu$m emission line from the Infrared Database of Extragalactic Observables from Spitzer…
This paper demonstrates that the stellar masses of galaxies in the Galaxy and Mass Assembly (GAMA) survey, originally derived via stellar population synthesis modelling, can be accurately predicted using only their absolute magnitudes and…
The spectra of Active Galactic Nuclei (AGNs) are often characterized by a wealth of emission lines with different profiles and intensity ratios that led to a complicated classification. Their electro-magnetic radiation spans more than 10…
In a complete sample of local 14-195 keV selected AGNs and inactive galaxies, matched by their host galaxy properties, we study the spatially resolved stellar kinematics and luminosity distributions at near-infrared wavelengths on scales of…
The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…
This paper pioneers the use of neural networks to provide a fast and automatic way to classify lightcurves in massive photometric datasets. As an example, we provide a working neural network that can distinguish microlensing lightcurves…
In this Master's project, the X-ray nuclear properties of a sample of bright nearby galaxies are explored. This is done by matching their comprehensive optical spectroscopic classification to the latest available XMM-Newton catalogue -…