Classification of pulsars with Dirichlet process Gaussian mixture model

Fahrettin Ay*, Gökhan Ince, Mustafa E. Kamaşak, K. Yavuz Ekşi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analysing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature, and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provide hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.

Original languageEnglish
Pages (from-to)713-722
Number of pages10
JournalMonthly Notices of the Royal Astronomical Society
Issue number1
Publication statusPublished - 1 Mar 2020

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society


KYE acknowledges support from TÜBITAK, The Scientific and Technological Research Council of Turkey, with the project number 118F028. We thank M. Ali Alpar and Erbil Gügercinog˘lu for useful discussion.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu118F028


    • Methods: data analysis
    • Methods: statistical
    • Pulsars: general
    • Stars: neutron


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