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A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training…
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…
Building a large sample of kiloparsec (kpc)-scale dual active galactic nuclei (AGNs) amongst merging galaxies is of vital importance to understand the co-evolution between host galaxies and their central super massive black holes (SMBHs).…
With the dual aims of enlarging the list of extremely metal-poor stars identified in the Galaxy, and boosting the numbers of moderately metal-deficient stars in directions that sample the rotational properties of the thick disk, we have…
We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an…
Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials are usually required to identify a suitable GNN architecture.…
Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys…
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance varies significantly depending on the function or…
Deep Neural Networks (DNNs) are rapidly being applied to safety-critical domains such as drone and airplane control, motivating techniques for verifying the safety of their behavior. Unfortunately, DNN verification is NP-hard, with current…
We use machine learning techniques to investigate their performance in classifying active galactic nuclei (AGNs), including X-ray selected AGNs (XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs). Using known physical…
A serious concern for semi-analytical galaxy formation models, aiming to simulate multi-wavelength surveys and to thoroughly explore the model parameter space, is the extremely time consuming numerical solution of the radiative transfer of…
Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care.…
We consider the techniques to distinguish normal star forming (NSF) galaxies and active galactic nuclei (AGN) hosts using optical spectra. The observational data base is a set of 20000 galaxies extracted from the Sloan Digital Sky Survey,…
Context. The North Ecliptic Pole (NEP) field provides a unique set of panchromatic data, well suited for active galactic nuclei (AGN) studies. Selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such method,…
Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified…
Algebraic multigrid (AMG) methods are among the most efficient solvers for linear systems of equations and they are widely used for the solution of problems stemming from the discretization of Partial Differential Equations (PDEs). The most…
(Abridged) We develop a novel technique to identify active galactic nuclei (AGNs) and study the nature of low-luminosity AGNs in the Sloan Digital Sky Survey. This is the first part of a series of papers and we develop a new, sensitive…
The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields like the Internet of Things (IoT) and Machine to Machine (M2M). However,…
Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual…
We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different…