Related papers: Interpreting automatic AGN classifiers with salien…
Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data…
In image classification task, feature extraction is always a big issue. Intra-class variability increases the difficulty in designing the extractors. Furthermore, hand-crafted feature extractor cannot simply adapt new situation. Recently,…
The incidence and properties of Active Galactic Nuclei (AGN) in the field, groups, and clusters can provide new information about how these objects are triggered and fueled, similar to how these environments have been employed to study…
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…
We develop a simple method to search for changing-look (CL) active galactic nucleus (AGN) candidates, and conduct a test run. In this method, optical variations of AGNs are monitored and CL-AGNs may appear to have a pattern of being bluer…
The finding of active galactic nuclei (AGN) in dwarf galaxies has important implications for galaxy evolution and supermassive black hole formation models. Yet, how AGN in dwarf galaxies form is still debated, in part due to scant…
In this paper, we investigate 2727 galaxies observed by MaNGA as of June 2016 to develop spatially resolved techniques for identifying signatures of active galactic nuclei (AGN). We identify 303 AGN candidates. The additional spatial…
We study the morphology of the central region of a sample of Active Galactic Nuclei (AGN) and a ``control'' sample of normal galaxies using archival observations of the WFPC2 instrument onboard the Hubble Space Telescope (HST). We use the…
Machine learning (ML) has seen significant growth in both popularity and importance. The high prediction accuracy of ML models is often achieved through complex black-box architectures that are difficult to interpret. This interpretability…
Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the…
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter. The variability patterns of the light emitted by an AGN contain information about…
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains. To handle large-scale graphs, most of the existing…
Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has multiple subtypes, the pathological mechanism of AD remains not fully understood and may…
The goal of this work is to develop accurate Machine Learning (ML) models for predicting the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by measurement data from historical cycles. The data-driven nature…
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher…
The changing-look active galactic nucleus (CL-AGN), an extraordinary subpopulation of supermassive black holes, has attracted growing attention for understanding its nature. We present an analysis of the spectral properties of 203…
The complexity of glasses makes it challenging to explain their dynamics. Machine Learning (ML) has emerged as a promising pathway for understanding glassy dynamics by linking their structural features to rearrangement dynamics. Support…
With upcoming all sky surveys such as LSST poised to generate a deep digital movie of the optical sky, variability-based AGN selection will enable the construction of highly-complete catalogs with minimum contamination. In this study, we…
Through the probing of light-matter interactions, Raman spectroscopy provides invaluable insights into the composition, structure, and dynamics of materials, and obtaining such data from portable and cheap instruments is of immense…
Saliency prediction is a well studied problem in computer vision. Early saliency models were based on low-level hand-crafted feature derived from insights gained in neuroscience and psychophysics. In the wake of deep learning breakthrough,…