Measuring the magnetic dipole moment and magnetospheric fluctuations of SXP 18.3 with a Kalman filter

J. O'Leary; A. Melatos; N. J. O'Neill; P. M. Meyers; D. M. Christodoulou; S. Bhattacharya; S. G. T. Laycock

doi:10.3847/1538-4357/ad2adc

paper

Measuring the magnetic dipole moment and magnetospheric fluctuations of SXP 18.3 with a Kalman filter

arXiv:2402.11991 · doi:10.3847/1538-4357/ad2adc

Abstract

The magnetic dipole moment $μ$ of an accretion-powered pulsar in magnetocentrifugal equilibrium cannot be inferred uniquely from time-averaged pulse period and aperiodic X-ray flux data, because the radiative efficiency $η_{0}$ of the accretion is unknown, as are the mass, radius, and distance of the star. The degeneracy associated with the radiative efficiency is circumvented, if fluctuations of the pulse period and aperiodic X-ray flux are tracked with a Kalman filter, whereupon $μ$ can be measured uniquely up to the uncertainties in the mass, radius, and distance. Here the Kalman filter analysis is demonstrated successfully in practice for the first time on Rossi X-ray Timing Explorer observations of the X-ray transient SXP 18.3 in the Small Magellanic Cloud, which is monitored regularly. The analysis yields $μ = 8. 0_{- 1.2}^{+ 1.3} \times 1 0^{30} G c m^{3}$ and $η_{0} = 0.0 4_{- 0.01}^{+ 0.02}$ , compared to $μ = 5. 0_{- 1.0}^{+ 1.0} \times 1 0^{30} G c m^{3}$ as inferred traditionally from time-averaged data assuming $η_{0} = 1$ . The analysis also yields time-resolved estimates of two hidden state variables, the mass accretion rate and the Maxwell stress at the disk-magnetosphere boundary. The success of the demonstration confirms that the Kalman filter analysis can be applied in the future to study the magnetic moments and disk-magnetosphere physics of accretion-powered pulsar populations in the Small Magellanic Cloud and elsewhere.

arXiv comment34 pages, 5 figures, accepted for publication in The Astrophysical Journal

Measuring the magnetic dipole moment and magnetospheric fluctuations of SXP 18.3 with a Kalman filter

Abstract

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