Related papers: Unsupervised Machine Learning for the Classificati…
Astronomers are increasingly faced with a deluge of information, and finding worthwhile targets of study in the sea of data can be difficult. Outlier identification studies are a method that can be used to focus investigations by presenting…
Forthcoming astronomical surveys are expected to detect new sources in such large numbers that measuring their spectroscopic redshift measurements will be not be practical. Thus, there is much interest in using machine learning to yield the…
The rapid increase in data on galaxy images at low and high redshift calls for re-examination of the classification schemes and for new automatic objective methods. Here we present a classification method by Artificial Neural Networks. We…
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a…
Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a…
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Learning (DL) based pipeline for detection, localization, and…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major…
The detection of new clusters of galaxies or the study of known clusters of galaxies in X-rays can be complicated by the presence of X-ray point sources, the majority of which will be active galactic nuclei (AGN). This can be addressed by…
We present a catalog of 9017 X-ray sources identified in Chandra observations of a 2 by 0.8 degree field around the Galactic center. We increase the number of known X-ray sources in the region by a factor of 2.5. The catalog incorporates…
The more than 80 nearby galaxies imaged with the Chandra Advanced CCD Imaging Spectrometer have been analyzed in a search for Ultra-Luminous X-ray (ULX) sources. The sample of galaxies span the range of Hubble morphological types and…
Approximately one-third of the gamma-ray sources in the third Fermi-LAT catalog are unidentified or unassociated with objects at other wavelengths. Observations with Swift-XRT have yielded possible counterparts in $\sim$30% of these source…
Using the Chandra Advanced CCD Imaging Spectrometer Imaging array (ACIS-I), we have carried out a deep hard X-ray observation of the Galactic plane region at (l,b) ~ (28.5, 0.0), where no discrete X-ray source had been reported previously.…
Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an…
We collect data at all frequencies for the new sources classified as unknown active galactic nuclei (AGNs) in the latest Burst Alert Telescope (BAT) all-sky hard X-ray catalog. Focusing on the 36 sources with measured redshift, we compute…
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we instead present and motivate a method for unsupervised…
Hyperluminous supersoft X-ray sources, such as bright extragalactic sources characterized by particularly soft X-ray spectra, offer a unique opportunity to study accretion onto supermassive black holes in extreme conditions. Examples of…
The X-ray populations of Local group galaxies have been classified in detail by Einstein, ROSAT and ASCA revealing a mix of binaries, supernova remnants and HII regions. However, these observatories were unable to resolve X-ray sources in…
In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…
We report the results of a programme of dual-epoch Chandra ACIS-S observations of five ultraluminous X-ray sources (ULXs) in nearby spiral galaxies. All five ULXs are detected as unresolved, point-like X-ray sources by Chandra, though two…