  We introduce deep-field metacalibration, a new technique that reduces the
pixel noise in metacalibration estimators of weak lensing shear signals by
using a deeper imaging survey for calibration. In standard metacalibration,
when estimating the object's shear response, extra noise is added to correct
the effect of shearing the noise in the image, increasing the uncertainty on
shear estimates by ~ 20%. Our new deep-field metacalibration technique
leverages a separate, deeper imaging survey to calculate calibrations with less
degradation in image noise. We demonstrate that weak lensing shear measurement
with deep-field metacalibration is unbiased up to second-order shear effects.
We provide algorithms to apply this technique to imaging surveys and describe
how to generalize it to shear estimators that rely explicitly on object
detection (e.g., metacalibration). For the Vera C. Rubin Observatory Legacy
Survey of Space and Time (LSST), the improvement in weak lensing precision will
depend on the somewhat unknown details of the LSST Deep Drilling Field (DDF)
observations in terms of area and depth, the relative point-spread function
properties of the DDF and main LSST surveys, and the relative contribution of
pixel noise vs. intrinsic shape noise to the total shape noise in the survey.
We conservatively estimate that the degradation in precision is reduced from
20% for metacalibration to ~ 5% or less for deep-field metacalibration, which
we attribute primarily to the increased source density and reduced pixel noise
contributions to the overall shape noise. Finally, we show that the technique
is robust to sample variance in the LSST DDFs due to their large area, with the
equivalent calibration error being ~ 0.1%. The deep-field metacalibration
technique provides higher signal-to-noise weak lensing measurements while still
meeting the stringent systematic error requirements of future surveys.
