Journal article
Searching for outliers in the Chandra Source Catalog
Monthly notices of the Royal Astronomical Society, Vol.516(3), pp.4324-4337
09/22/2022
DOI: 10.1093/mnras/stac2481
Abstract
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 a smaller set of sources that could prove interesting because they do not follow the trends of the underlying population. We apply a principal component analysis (PCA) and an unsupervised random forest algorithm (uRF) to sources from the Chandra Source Catalog v.2 (CSC2). We present 119 high-significance sources that appear in all repeated applications of our outlier identification algorithm (OIA). We analyse the characteristics of our outlier sources and cross-match them with the SIMBAD data base. Our outliers contain several sources that were previously identified as having unusual or interesting features by studies. This OIA leads to the identification of interesting targets that could motivate more detailed study.
Details
- Title: Subtitle
- Searching for outliers in the Chandra Source Catalog
- Creators
- Dustin K. Swarm - University of IowaC. T. DeRoo - University of IowaY. Liu - University of IowaS. Watkins - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Monthly notices of the Royal Astronomical Society, Vol.516(3), pp.4324-4337
- Publisher
- Oxford Univ Press
- DOI
- 10.1093/mnras/stac2481
- ISSN
- 0035-8711
- eISSN
- 1365-2966
- Number of pages
- 14
- Grant note
- Iowa Initiative for Artificial Intelligence NNX16AL88H / Iowa Space Grant Consortium under NASA University of Iowa College of Liberal Arts and Sciences
- Language
- English
- Date published
- 09/22/2022
- Academic Unit
- Physics and Astronomy; University College Courses
- Record Identifier
- 9984428793502771
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