English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Low Mass X-ray binaries (LMXBs) are binary systems where one of thecomponents is either a black hole or a neutron star and the other is a lessmassive star. It is challenging to unambiguously determine whether a LMXB hostsa black hole or a neutron star. In the last few decades, multiple observationalworks have tried, with different levels of success, to address this problem. Inthis paper, we explore the use of machine learning to tackle this observationalchallenge. We train a random forest classifier to identify the type of compactobject using the energy spectrum in the energy range 5-25 keV obtained from theRossi X-ray Timing Explorer archive. We report an average accuracy of 87+/-13in classifying the spectra of LMXB sources. We further use the trained modelfor predicting the classes for LMXB systems with unknown or ambiguousclassification. With the ever-increasing volume of astronomical data in theX-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM,ATHENA, NICER), such methods can be extremely useful for faster and robustclassification of X-ray sources and can also be deployed as part of the datareduction pipeline.

A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature