Astrophysics> High Energy Astrophysical Phenomena
arXiv:2206.13656 (astro-ph)
[Submitted on 27 Jun 2022 (v1), last revised 21 Jul 2023 (this version, v3)]
Title:Classifying Unidentified X-ray Sources in the Chandra Source Catalog Using a Multiwavelength Machine-learning Approach
View PDFAbstract:The rapid increase in serendipitous X-ray source detections requires the development of novel approaches to efficiently explore the nature of X-ray sources. If even a fraction of these sources could be reliably classified, it would enable population studies for various astrophysical source types on a much larger scale than currently possible. Classification of large numbers of sources from multiple classes characterized by multiple properties (features) must be done automatically and supervised machine learning (ML) seems to provide the only feasible approach. We perform classification of Chandra Source Catalog version 2.0 (CSCv2) sources to explore the potential of the ML approach and identify various biases, limitations, and bottlenecks that present themselves in these kinds of studies. We establish the framework and present a flexible and expandable Python pipeline, which can be used and improved by others. We also release the training data set of 2941 X-ray sources with confidently established classes. In addition to providing probabilistic classifications of 66,369 CSCv2 sources (21% of the entire CSCv2 catalog), we perform several narrower-focused case studies (high-mass X-ray binary candidates and X-ray sources within the extent of the H.E.S.S. TeV sources) to demonstrate some possible applications of our ML approach. We also discuss future possible modifications of the presented pipeline, which are expected to lead to substantial improvements in classification confidences.
| Comments: | Published in ApJ 941, 104 (2022). The data and software used in this paper are available at this https URL and also on Vizier this https URL. The interactive training dataset visualization is available at this https URL |
| Subjects: | High Energy Astrophysical Phenomena (astro-ph.HE) |
| Cite as: | arXiv:2206.13656 [astro-ph.HE] |
| (or arXiv:2206.13656v3 [astro-ph.HE] for this version) | |
| https://doi.org/10.48550/arXiv.2206.13656
arXiv-issued DOI via DataCite
|
|
| Journal reference: | ApJ, Volume 941, Dec 2022, Pages 104 |
| Related DOI: | https://doi.org/10.3847/1538-4357/ac952b
DOI(s) linking to related resources
|
Submission history
From: Hui Yang [view email][v1] 2022年6月27日 22:50:55 UTC (6,280 KB)
[v2] 2022年9月21日 14:20:09 UTC (6,302 KB)
[v3] 2023年7月21日 13:49:46 UTC (6,269 KB)
Current browse context:
astro-ph.HE
Change to browse by:
References & Citations
BibTeX formatted citation
×
Data provided by:
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.