Off-topic for aiqbench
This story was gathered from a source we follow but falls outside AI and quantum coverage, so there is no aiqbench take for it.
Spatial-Mode Selection for Local Mineral Absorption Spectroscopy
arXiv:2610.39221v1 Announce Type: cross Abstract: Absorption-band depth measures the fractional decrease in mineral reflectance below its local continuum. Recovering this feature strength for a small mineral region supports quantitative comparisons between regions, but diffraction mixes its light with the surroundings. Conventional spectral fitting can separate mean contributions; it cannot remove background photon noise already recorded. We develop a quantum-inspired measurement framework that uses spatial-mode demultiplexing (SPADE) to retain the target absorption response while reducing background acceptance before detection. The framework connects finite mineral-region geometry and reference spectra to estimation precision. A loss-dependent design condition identifies when adding SPADE exposure improves on direct imaging (DI) under fixed-response Poisson measurements with unknown scene parameters. Joint inference from initial DI observations and both mode ports estimates target depth together with an unknown, spatially shared background spectrum. In equal-time simulations that include localization uncertainty, alignment time, calibration, and detector costs, this acquisition reduces depth root-mean-square error (RMSE) by 14\%--26\% across five absorption strengths relative to all-pixel DI with the same background freedom. These results use a supplied target feature shape and geometry, with target continuum slope fitted and curvature fixed. The analysis separates optical selectivity from parameter ambiguity and acquisition cost, providing a quantitative basis for spatial-mode receiver design in local mineral spectroscopy.
Abstract:Absorption-band depth measures the fractional decrease in mineral reflectance below its local continuum. Recovering this feature strength for a small mineral region supports quantitative comparisons between regions, but diffraction mixes its light with the surroundings. Conventional spectral fitting can separate mean contributions; it cannot remove background photon noise already recorded. We develop a quantum-inspired measurement framework that uses spatial-mode demultiplexing (SPADE) to retain the target absorption response while reducing background acceptance before detection. The framework connects finite mineral-region geometry and reference spectra to estimation precision. A loss-dependent design condition identifies when adding SPADE exposure improves on direct imaging (DI) under fixed-response Poisson measurements with unknown scene parameters. Joint inference from initial DI observations and both mode ports estimates target depth together with an unknown, spatially shared background spectrum. In equal-time simulations that include localization uncertainty, alignment time, calibration, and detector costs, this acquisition reduces depth root-mean-square error (RMSE) by 14\%--26\% across five absorption strengths relative to all-pixel DI with the same background freedom. These results use a supplied target feature shape and geometry, with target continuum slope fitted and curvature fixed. The analysis separates optical selectivity from parameter ambiguity and acquisition cost, providing a quantitative basis for spatial-mode receiver design in local mineral spectroscopy.
Optics (physics.optics); Quantum Physics (quant-ph)
arXiv:2610.39221 [physics.optics]
(or arXiv:2610.39221v1 [physics.optics] for this version)
https://doi.org/10.48550/arXiv.2610.39221 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 300 words