  High spatial resolution of ultracold neutron (UCN) measurement is of growing
interest to UCN experiments such as UCN spectrometers, UCN polarimeters,
quantum physics of UCNs, and quantum gravity. Here we utilize physics-informed
deep learning to enhance the experimental position resolution and to
demonstrate sub-micron spatial resolutions for UCN position measurements
obtained using a room-temperature CMOS sensor, extending our previous work [1,
2] that demonstrated a position uncertainty of 1.5 microns. We explore the use
of the open-source software Allpix Squared to generate experiment-like
synthetic hit images with ground-truth position labels. We use physics-informed
deep learning by training a fully-connected neural network (FCNN) to learn a
mapping from input hit images to output hit position. The automated analysis
for sub-micron position resolution in UCN detection combined with the fast data
rates of current and next generation UCN sources will enable improved precision
for future UCN research and applications.
