Self-Bayesian Aberration Removal via Constraints for Ultracold Atom Microscopy

Published

October 29, 2021

Author(s)

Ian Spielman, Emine Altuntas

Abstract

High-resolution imaging of ultracold atoms typically requires custom high numerical aperture (NA) optics, as is the case for quantum gas microscopy. These high NA objectives involve many optical elements each of which contributes to loss and light scattering, making them unsuitable for quantum back-action limited "weak" measurements. We employ a low cost high NA aspheric lens as an objective for a practical and economical---although aberrated---high resolution microscope to image 87Rb Bose-Einstein condensates. Here, we present a novel methodology for digitally eliminating the resulting aberrations that is applicable to a wide range of imaging strategies and requires no additional hardware. We recover nearly the full NA of our objective, thereby demonstrating a simple and powerful digital aberration correction method for achieving optimal microscopy of quantum objects. This reconstruction relies on a high quality measure of our imaging system's even-order aberrations from density-density correlations measured with differing degrees of defocus. We demonstrate our aberration compensation technique using phase contrast imaging, a dispersive imaging technique directly applicable to quantum back-action limited measurements. Furthermore, we show that our digital correction technique reduces the contribution of photon shot noise to density-density correlation measurements which would otherwise contaminate the desired quantum projection noise signal in weak measurements.

Citation

Physical Review Research

Pub Type

Journals

Download Paper

Local Download

Keywords

ultracold atoms, microscopy, weak measurement, digitally optimized microscopy

Citation

Spielman, I. and Altuntas, E. (2021), Self-Bayesian Aberration Removal via Constraints for Ultracold Atom Microscopy, Physical Review Research, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933115 (Accessed December 18, 2024)