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The hunt for new pulsating ultraluminous X-ray sources: a clustering approach
Created by
Haebom
Author
Nicol o Oreste Pinciroli Vago, Roberta Amato, Matteo Imbrogno, GianLuca Israel, Andrea Belfiore, Konstantinos Kovlakas, Piero Fraternali, Mario Pasquato
Outline
This paper presents an artificial intelligence (AI)-based approach to identify pulsating PULX candidates among ultra-luminosity X sources (ULXs). Using the XMM-Newton database, an unsupervised clustering algorithm is applied to classify celestial objects with similar characteristics into two clusters. The separation criteria between the two clusters are established using known PULX observation data, and the clusters containing new PULX candidates are identified. As a result, 85 unique objects (355 observations) are selected as new PULX candidates, and about 85% of them have been observed multiple times. However, preliminary time series analysis did not detect any new pulsation signals. This study presents a new set of PULX candidates with similar characteristics to known PULXs in multidimensional phase space despite the absence of pulsations in their light curves.
Takeaways, Limitations
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Takeaways: We demonstrate that AI-based methods can be used to efficiently screen novel PULX candidates. We also suggest future research directions by suggesting PULX candidates that are difficult to discover using existing methods.
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Limitations: Preliminary time series analysis of the selected candidates did not reveal any new pulsation signals. Additional observational data is needed, and additional observations and analyses are needed to verify the robustness of the AI-based method. Whether the selected candidates are real PULXs requires further verification.
