Modelling the Axion-Photon Conversion in Neutron Star Populations

  • Utkarsh Bhura

Student thesis: Master of Science by Research


Axions, proposed as a solution to the strong-CP problem in particle physics, have gained significant attention as potential dark matter candidates in the field of astronomy. Axions can readily resonate into photons in the presence of high magnetic fields, a condition ob- served commonly in neutron stars. This work delves into a comprehensive investigation of modeling the axion-photon resonant signal emanating from a population of neutron stars, aiming to unravel the factors dictating signal properties. The neutron star popula- tion is simulated using the versatile PsrPopPy Python package, providing a valuable tool for this study. A comparative analysis between our population study and a ‘single star analysis’ is conducted to discern the superior approach. Notably, the study reveals that the three most influential contributors to the signal are the magnetic field, the distance of the star from the Galactic Center (GC), and the number of stars in the population. The dependencies on magnetic field and distance can be correlated with the Goldreich-Julian charge density and the dark matter density profile, respectively. The GC magnetar, PSR J1745-2900, emerges as a prominent candidate due to its high magnetic field and prox- imity to the GC. This magnetar proves to be exceedingly effective in imposing stringent constraints on the coupling constant, overshadowing the entire population of pul- sars generated by PsrPopPy. The influence of the third parameter, the number of stars, is elucidated through a simple, straightforward model survey of the Galactic center. This survey attempts to impose a 2-sigma constraint on coupling constant, and the results are compared with a recent radio survey for pulsars in the GC. The thesis also comments on the uncertainties in recent surveys when modeling the resonant signal from the GC, highlighting the key challenges and limitations in modelling the neutron stars at the GC
Date of Award1 Aug 2024
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorChristopher Conselice (Supervisor) & Richard Battye (Supervisor)

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