Related papers: Interpreting automatic AGN classifiers with salien…
In this study, we use the SWIFT/BAT AGN sample, which has received extensive multiwavelength follow-up analysis as a result of the BAT AGN Spectroscopic Survey (BASS), to develop a diagnostic for nuclear obscuration by examining the…
Active Galactic Nuclei (AGN) are axisymmetric systems to first order; their observed properties are likely strong functions of inclination with respect to our line of sight, yet the specific inclinations of all but a few AGN are generally…
We used data from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) to study the incidence of AGN in continuum-selected galaxies at $z\sim3$. From optical and infrared imaging in the 24 deg$^{2}$ Spitzer HETDEX Exploratory Large…
Variability studies hold information on otherwise unresolvable regions in Active Galactic Nuclei (AGN). Population studies of large samples likewise have been very productive for our understanding of AGN. These two themes are coming…
The photoionization model of narrow-line regions (NLRs) in active galactic nuclei (AGNs) has been investigated for decades. Many published models are restricted to simple linear scaling abundance relations, dust-free assumption, uniform AGN…
Variability is a property shared by virtually all active galactic nuclei (AGNs), and was adopted as a criterion for their selection using data from multi epoch surveys. Low Luminosity AGNs (LLAGNs) are contaminated by the light of their…
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us…
We present the multi-wavelength and environmental properties of 37 variability-selected active galactic nuclei (AGNs), including 30 low luminosity AGNs (LLAGNs), using a high cadence time-domain survey (ASAS-SN) from a spectroscopic sample…
Saliency map estimation in computer vision aims to estimate the locations where people gaze in images. Since people tend to look at objects in images, the parameters of the model pretrained on ImageNet for image classification are useful…
Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic segmentation in…
The last decades have seen great progress in saliency prediction, with the success of deep neural networks that are able to encode high-level semantics. Yet, while humans have the innate capability in leveraging their knowledge to decide…
Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of what they learn from the data and makes their application to…
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to…
We present modeling and interpretation of the continuum and emission lines for a sample of 51 unobscured Type 1 active galactic nuclei (AGN). All of these AGNs have high quality spectra from both XMM-Newton and Sloan Digital Sky Survey…
Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such…
Determining the frequency and duration of changing--look (CL) active galactic nuclei (AGNs) phenomena, where the optical broad emission lines appear or disappear, is crucial to understand the evolution of the accretion flow around…
Machine learning (ML) is quickly emerging as a powerful tool with diverse applications across an extremely broad spectrum of disciplines and commercial endeavors. Typically, ML is used as a black box that provides little illuminating…
Currently available methods for extracting saliency maps identify parts of the input which are the most important to a specific fixed classifier. We show that this strong dependence on a given classifier hinders their performance. To…
In order to analyse the large numbers of Seyfert galaxy spectra available at present, we are testing new techniques to derive their physical parameters fastly and accurately. We present an experiment on such a new technique to segregate old…
Recently, many methods to interpret and visualize deep neural network predictions have been proposed and significant progress has been made. However, a more class-discriminative and visually pleasing explanation is required. Thus, this…