Speaker
Description
The axion is a compelling hypothetical particle that could account for some or all of the dark matter in our universe, while simultaneously explaining why quark interactions within the neutron do not appear to give rise to an electric dipole moment. The most sensitive axion detection technique in the 4-40 ueV mass range makes use of the axion-photon coupling and is called the axion cavity haloscope. Via the inverse-Primakoff effect, axions are resonantly converted into detectable microwave photons in a tunable, high-Q, resonant cavity permeated by a strong static magnetic field. As searches scan up in axion mass, future detectors will need to combat a dramatic loss in sensitivity due to shrinking cavity volume & cavity mode Q, and increased mode+receiver noise at the standard quantum limit. Overcoming these challenges will involve a synthesis of technical solutions, ranging from the lock-step tuning of cavity array networks to novel quantum sensing approaches surpassing the standard quantum limit. Unfortunately, this trend will likely push the designs of planned axion searches into an intractable level of intricacy. This talk presents efforts at PNNL to offset this anticipated increase in detector complexity by leveraging a variety of AI/ML methods to develop new tools for characterizing, diagnosing, and controlling low temperature RF experiments immersed in strong magnetic fields. I will give an overview of axion dark matter detection, explore challenges facing the next generation of microwave quantum-sensing-based searches, and discuss proof-of-concept demonstrations that point towards AI/ML as a key to enabling axion discovery at high masses.
