Authors: Stefan Knirck et al. (BREAD Collaboration)
First Author’s Institution: Fermi National Accelerator Laboratory, Batavia, Illinois, USA
Status: Published in PRL [open access]
Dark matter is a mystery of modern physics that has eluded scientists for nearly a century. It doesn’t absorb, reflect, or emit light , making it essentially invisible, but its observed gravitational effects on stars and galaxies lead scientists to believe that something is there. The question is: what is that something made of? No particle in the Standard Model has characteristics matching those of dark matter, so there must be a new, undiscovered particle that does. Scientists have proposed a variety of different dark matter candidates that span over eighty orders of magnitude in mass. One such candidate is the axion, which is thought to be lightweight dark matter that acts like a wave oscillating at a specific frequency.
To search for this type of dark matter, researchers use resonant cavities. These cavities are extremely sensitive to stimuli that oscillate at a particular frequency, and ignore stimuli oscillating at all other frequencies. But because resonant cavities only work over a narrow range of frequencies, many mass ranges remain unsearched. To address this, researchers have developed a new experiment to search for dark matter: the Broadband Experiment for Axion Detection, or BREAD. BREAD takes advantage of the fact that when wavelike dark matter, such as a dark photon, hits a metallic surface, it can convert to photons. For a surface much larger than the photon’s wavelength, this conversion doesn’t depend on the dark photon’s mass, allowing this detection method to overcome the limitations of resonant cavities. In today’s paper, the authors report the first results of GigaBREAD, a proof-of-concept version of BREAD.
The experimental setup of GigaBREAD is shown in Figure 1. It consists of a teardrop-shaped mirror enclosed within a cylindrical aluminum barrel, with a horn antenna positioned at a focal point of the system. As a dark photon collides with the barrel, a photon is emitted perpendicularly from the barrel’s inner surface, and the curved mirror focuses it to the horn antenna. The signal detected at the horn antenna is then fed to a pipeline of data acquisition electronics and is converted to a power spectrum, which indicates the power of different frequency components in the signal. Without the detection of dark matter, the signal will just contain noise. If dark matter is detected, then there will be excess power at a particular frequency, resulting in a peak that exceeds the noise.
The authors collected data from this system for about a month. In addition to allowing the system to try and detect dark matter, they also injected two fake signals into the system to test how well their experiment can identify a signal. The authors found they were able to identify the frequencies of the injected signals with 30σ and 40σ significance (i.e. very well). After removing the data from the injected signals (shown in Figure 2), the largest excess power in the signal was observed at 10.829 GHz with 2σ significance, which is too small to claim a detection. Using this measurement, the authors were able to calculate an upper bound on the mixing parameter between photons and dark photons χ, a quantity that describes the conversion of dark photons to photons. They found χ≲10-12 for masses of 44-52 μeV at a 90% confidence level. Figure 3 shows this constraint on χ in the context of results from other dark matter searches. You can see that the GigaBREAD result surpasses the bounds placed by cosmology by more than two orders of magnitude and provides the most sensitive constraint for masses between 44 and 49 μeV. These results indicate that GigaBREAD is a successful proof-of-concept of BREAD’s innovative dark matter detection method, and we eagerly await future results.
Astrobite edited by Caroline von Raesfeld
Featured image credit: Adapted from Wikimedia Commons and Figure 1 of today’s paper.
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