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X-ray annual modulation observed by XMM-Newton and Axion Quark Nugget Dark Matter

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 Publication date 2020
  fields Physics
and research's language is English




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The XMM-Newton observatory shows evidence with an $11 sigma$ confidence level for seasonal variation of the X-ray background in the near-Earth environment in the 2-6 keV energy range (Fraser et al. 2014). The interpretation of the seasonal variation given in Fraser et al. 2014 was based on the assumption that solar axions convert to X-rays in the Earths magnetic field. There are many problems with this interpretation, since the axion-photon conversion must preserve the directionality of the incoming solar axion. At the same time, this direction is avoided by the observations because the XMM-Newtons operations exclude pointing at the Sun and at the Earth. The observed seasonal variation suggests that the signal could have a dark matter origin, since it is very difficult to explain with conventional astrophysical sources. We propose an alternative explanation which involves the so-called Axion Quark Nugget (AQN) dark matter model. In our proposal, dark matter is made of AQNs, which can cross the Earth and emit high energy photons at their exit. We show that the emitted intensity and spectrum is consistent with Fraser et al. 2014, and that our calculation is not sensitive to the specific details of the model. We also find that our proposal predicts a large seasonal variation, on the level of 20-25%, much larger than conventional dark matter models (1-10%). Since the AQN emission spectrum extends up to $sim$100 keV, well beyond the keV sensitivity of XMM-Newton, we predict the AQN contribution to the hard X-ray and $gamma$-ray backgrounds in the Earths environment. The Gamma-Ray Burst Monitor instrument (GBM), aboard the Fermi telescope, is sensitive to the 8 keV-40 MeV energy band. We suggest that the multi-year archival data from the GBM could be used to search for a seasonal variation in the near-Earth environment up to 100 keV as a future test of the AQN framework.



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