The Dark-OST project aims to search for ultralight cosmic particles that are hypothesized to be the constituent of Dark Matter, the dominant yet elusive part of the matter in our Universe. Discovering such particles will also help answer other important questions in modern science, including: What is the reason of the observed predominance of matter over antimatter in the Universe? What is the mechanism of the CP violation? Why are all observed elementary particles so light compared to the fundamental energy scales (the grand-unification scale and the Planck scale)?
Understanding of the most fundamental laws of nature is a high intellectual pursuit that has led to revolutionary technological advances along the way. In Dark-OST, we are developing novel magnetic-resonance technologies that will be useful in applications to chemistry and biology, apart from advancing fundamental science. See image001.png - Caption: conceptual illustration of the GNOME concept. Shielded atomic magnetometers spread around the globe detect the passage of a domain wall of a pseudoscalar field (the dark matter candidate), forming a signal pattern that depends on the direction and speed of the wall.
In addition to these major scientific goals, we also seek to address the following questions:
What are the ultimate limits of sensitivity of quantum sensors? How do these limits depend on the type of the signal, for example, those due to actual magnetic perturbations as opposed to the “pseudomagnetic” ones, such as those due to exotic fields (e.g. dark matter), or in a more practical context, due to platform rotation.
What is the intrinsic relation between possible dark matter signals of different types, for instance, due to the simultaneous presence of pseudoscalar couplings (affecting particles’ spins) and scalar couplings (manifesting as an apparent variation of fundamental ``constants’’). What are the benefits of hybrid networks, for example, those incorporating magnetometers, atomic clocks, interferometers, etc.?
What are the optimal configurations of sensor networks? How can one extract maximal possible information from a network of sensors in the presence of various kinds of noise sources? This question is equally important for fundamental physics and practical applications, for instance, measuring feeble magnetic fields from the brain with a network of sensors around the patient’s head.
