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  • Open Access

Proposed network to detect axion quark nugget dark matter

Xunyu Liang, Egor Peshkov, Ludovic Van Waerbeke, and Ariel Zhitnitsky
Phys. Rev. D 103, 096001 – Published 3 May 2021
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  • INTRODUCTION
  • THE AQN FRAMEWORK
  • WHEN THE AQN HITS THE EARTH…
  • TRAJECTORY AND VELOCITY RECONSTRUCTION
  • CONCLUSION
  • ACKNOWLEDGMENTS
  • APPENDICES
    • References

    Abstract

    A network of synchronized detectors can increase the likelihood of discovering the QCD axion within the axion quark nugget (AQN) dark matter model. A similar network can also discriminate the x rays emitted by the AQNs from the background signal. These networks can provide information on the directionality of the dark matter flux (if any), as well as its velocity distribution, and can therefore test the Standard Halo Model. We show that the optimal configuration to detect AQN-induced axions is a triangular network of stations 100 km apart. For x rays, the optimal network is an array of tetrahedral units.

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    • Received 21 December 2020
    • Accepted 12 April 2021

    DOI:https://doi.org/10.1103/PhysRevD.103.096001

    Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

    Published by the American Physical Society

    Physics Subject Headings (PhySH)

    1. Research Areas
    AxionsCosmologyDark matterParticle dark matter
    Particles & FieldsGravitation, Cosmology & Astrophysics

    Authors & Affiliations

    Xunyu Liang*, Egor Peshkov†, Ludovic Van Waerbeke‡, and Ariel Zhitnitsky§

    • Department of Physics and Astronomy, University of British Columbia, Vancouver V6T 1Z1, Canada

    • *xunyul@phas.ubc.ca
    • †e.peshkov@alumni.ubc.ca
    • ‡waerbeke@phas.ubc.ca
    • §arz@phas.ubc.ca

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    Issue

    Vol. 103, Iss. 9 — 1 May 2021

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    Images

    • Figure 1
      Figure 1

      The AQN induced x rays in the atmosphere could be observed by nearby surface detectors. At the same time, the AQN-induced axions and neutrinos are mostly produced in the interior due to a much higher density of the surrounding material. These axions and neutrinos can traverse entire Earth to be detected by instruments located on the surface.

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    • Figure 2
      Figure 2

      The spectral surface emissivity of an AQN with the suppression effects at ω≪T. The top plot corresponds to T=10  keV, while the bottom plot corresponds to T=50  keV. Adopted from [13].

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    • Figure 3
      Figure 3

      The normalized velocity distribution. The plot is adopted from [18]. The four different curves reflect the dependence on model parameters.

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    • Figure 4
      Figure 4

      Signal (axion or x ray) of a local flash observed by a detector. The burstlike signal peaks at time t, with a bandwidth τ.

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    • Figure 5
      Figure 5

      Coordinate system used in Eq. (17). An AQN passes nearby three stations (gray circles) located at Ri (i=1, 2, 3), along a linear trajectory r(t) (dashed). The distance from each station to r(t) is denoted by di (i=1, 2, 3), respectively.

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    • Figure 6
      Figure 6

      Two network configurations for the detection of axions: (a) an equilateral triangle and (b) a square network. Optimally, stations (gray circle) are separated by a distance d≃100  km. 94% (resp. 100%) of the trajectories are uniquely determined in a triangular (resp. square) network. A triangular network has the highest event rate per station.

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    • Figure 7
      Figure 7

      A base unit of the x-ray array of detectors. It consists of four detectors (grey circles) located at the vertices of a regular tetrahedral, separated by d=50  m.

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    • Figure 8
      Figure 8

      Qualitative picture of the four degenerate solutions (Z2×Z2) in Eq. (17). Allowed trajectories are simultaneously tangent to three spheres of given radii di (i=1, 2, 3), where di is the distance of the AQN trajectory from the ith detector and is determined by the input parameter (τi,ti). In practice, the radii change moderately upon the modified time reversal T transformation to account for the rescaled (v,d1).

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    • Figure 9
      Figure 9

      Histogram of C fitting into inequality (21) in Monte Carlo simulation (106 samples). The parameter ϵcut is chosen to be 0.5 and the count is normalized. The value of C is found to be 1.24 (1.92) among 95% (99%) of samples.

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    • Figure 10
      Figure 10

      Histogram of ⟨(R^·v^)−1⟩ as defined in Eq. (23). Monte Carlo simulation (106 samples). The count is normalized. The value of ⟨(R^·v^)−1⟩ is found to be 12.3 (resp. 32.0) among 95% (resp. 99%) of samples.

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    • Figure 11
      Figure 11

      Signals generated for a triangular configuration. Each signal corresponds to each station detecting the axion flux due to a nearby AQN. From these signals, we can obtain the parameters ti and τi in order to solve the system of Eq. (17).

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