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Search for transient ultralight dark matter signatures with networks of precision measurement devices using a Bayesian statistics method

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arxiv 1803.10264 v1 pith:LFIN7UDK submitted 2018-03-27 astro-ph.IM astro-ph.COphysics.atom-ph

classification astro-ph.IMastro-ph.COphysics.atom-ph
keywords atomicmethodclocksdarkclockdatadevicesmatter
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We analyze the prospects of employing a distributed global network of precision measurement devices as a dark matter and exotic physics observatory. In particular, we consider the atomic clocks of the Global Positioning System (GPS), consisting of a constellation of 32 medium-Earth orbit satellites equipped with either Cs or Rb microwave clocks and a number of Earth-based receiver stations, some of which employ highly-stable H-maser atomic clocks. High-accuracy timing data is available for almost two decades. By analyzing the satellite and terrestrial atomic clock data, it is possible to search for transient signatures of exotic physics, such as "clumpy" dark matter and dark energy, effectively transforming the GPS constellation into a 50,000km aperture sensor array. Here we characterize the noise of the GPS satellite atomic clocks, describe the search method based on Bayesian statistics, and test the method using simulated clock data. We present the projected discovery reach using our method, and demonstrate that it can surpass the existing constrains by several order of magnitude for certain models. Our method is not limited in scope to GPS or atomic clock networks, and can also be applied to other networks of precision measurement devices.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Applying the matched-filter technique to the search for dark matter transients with networks of quantum sensors

    astro-ph.IM 2019-08 conditional novelty 6.0 of 10

    A matched-filter search statistic for dark matter transients in GPS atomic clock networks is developed, with analytic sensitivity formulas and simulation-based detection thresholds.

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