Neural Fields and Differentiable Programming for Quantum Sensing in the MAGIS Experiment
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 techniques offer innovative and powerful solutions to this diverse set of problems. In this talk, 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.