Advancements in Dark Matter Detection Using Quantum Sensor Networks

A recent paper titled "Long-baseline quantum sensor network as dark matter haloscope" by Min Jiang and colleagues presents significant advancements in the search for dark matter. The research focuses on ultralight dark photons, which are considered a viable candidate for dark matter. The study describes the first search for correlated dark-photon signals utilizing a long-baseline network consisting of 15 atomic magnetometers. These sensors are strategically placed in two separate meter-scale shield rooms, approximately 1700 kilometers apart.

The network's design enhances the detection of dark-photon electromagnetic signals while effectively minimizing local noise sources through long-baseline measurements. The findings indicate that the kinetic mixing coefficient of dark photon dark matter has been constrained over a mass range of 4.1 femto-electronvolts (feV) to 2.1 pico-electronvolts (peV). This represents the most stringent constraints achieved from any terrestrial experiments within this mass range.

The implications of this research extend beyond current findings, as future data releases from this network may surpass existing astrophysical constraints derived from the cosmic microwave background and plasma heating. This advancement could potentially lead to new insights into the nature of dark matter and its interactions with standard model particles.

For further details, the paper can be accessed at arXiv:2305.00890.