Artificial Intelligence for Atom Interferometry (AI^2)

MAGIS-100 is a proposed experiment under construction at Fermilab which will use interference fringes imprinted on cold atom clouds to sense physics signals, such as mid-frequency band gravitational waves and ultralight dark matter. To maximize the reach of this new experiment, a sophisticated set of tools must be developed for imaging, data reconstruction, and simulation. Modern machine learning/AI techniques offer innovative and powerful solutions to this diverse set of problems. In this poster, we present 3D reconstruction techniques for atom clouds using a differentiable ray-tracing simulator in conjunction with methods from modern neural rendering. Such techniques, used along with a recently developed light-field imaging device [arXiv:2205.11480], enable 3D reconstruction of cold atom clouds in a single camera shot. We further present a differentiable atomic simulator, which characterizes a dominant experimental systematic via a gradient-based fitting of wavefront aberrations in the lasers used for the interferometry. Several extensions to the above work are also discussed.