Issue |
A&A
Volume 690, October 2024
|
|
---|---|---|
Article Number | A51 | |
Number of page(s) | 24 | |
Section | Astrophysical processes | |
DOI | https://doi.org/10.1051/0004-6361/202449953 | |
Published online | 01 October 2024 |
Bayesian sensitivity of binary pulsars to ultra-light dark matter
1
CEICO, FZU–Institute of Physics of the Czech Academy of Sciences, Na Slovance 2, 182 00 Praha 8, Czech Republic
2
Charles University, Faculty of Mathematics and Physics, Institute of Theoretical Physics, V Holešovičkách 2, 180 00 Praha 8, Czech Republic
3
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina
4
CONICET – Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
Received:
12
March
2024
Accepted:
5
June
2024
Ultra-light dark matter perturbs the orbital motion of binary pulsars, in particular by causing peculiar time variations of a binary’s orbital parameters, which then induce variations in the pulses’ times of arrival. Binary pulsars have therefore been shown to be promising detectors of ultra-light dark matter. To date, the sensitivity of binary pulsars to ultra-light dark matter has only been studied for dark matter masses in a narrow resonance band around a multiple of the binary pulsar orbital frequency. In this study we devise a two-step, bayesian method that enables us to compute semi-analytically the sensitivity for all masses, also away from the resonance, and to combine several observed binaries into one global sensitivity curve. We then apply our method to the case of a universal, linearly-coupled, scalar ultra-light dark matter. We find that with next-generation radio observatories the sensitivity to the ultra-light dark matter coupling will surpass that of Solar-System constraints for a decade in mass around m ∼ 10−21 eV, even beyond resonance
Key words: binaries: general / pulsars: general / dark matter
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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