Differential source-basis encoding for superresolved parameter estimation in a time-reversed Young interferometer
- URL: http://arxiv.org/abs/2603.27407v1
- Date: Sat, 28 Mar 2026 21:05:36 GMT
- Title: Differential source-basis encoding for superresolved parameter estimation in a time-reversed Young interferometer
- Abstract summary: We develop a differential source-encoding protocol for local parameter estimation in a time-reversed Young interferometer.<n>In the local regime, the differential signal separates naturally into an envelope-gradient term, which is also present in noninterferometric differential sensing.<n>This decomposition identifies the physical origin of the interferometric advantage and clarifies why it is regime dependent rather than universal.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We develop a differential source-encoding protocol for local parameter estimation in a time-reversed Young interferometer, where the source plane is used not merely as a scan coordinate but as a programmable measurement basis. Two sequential positive-only source patterns implement an antisymmetric differential probe about a chosen operating point, converting the deterministicc source-coordinate response into a derivative-gradient sensing channel. In the local regime, the differential signal separates naturally into an envelope-gradient term, which is also present in noninterferometric differential sensing, and an interference-gradient term, which is specific to the time-reversed Young fringe law. This decomposition identifies the physical origin of the interferometric advantage and clarifies why it is regime dependent rather than universal. Using a shot-noise-limited Poisson model, we derive the corresponding Fisher information and Cramér--Rao bounds and compare the protocol with raster sampling in the same geometry and with a matched noninterferometric differential baseline. Representative numerical examples show a strong and robust gain over raster sampling, while the additional improvement from the time-reversed Young interference is parameter dependent but can be substantial in favorable regimes. The results establish the time-reversed Young geometry as a practically simple platform for programmable differential interferometric metrology.
Related papers
- High-Dimensional Change-Point Detection via Angular Kernel Statistics [9.486498387993718]
We study change-point detection for high-dimensional data in regimes where inference must be performed from small batches of observations.<n>Our primary focus is the high-dimensional, low sample size (HDLSS) regime, where the sequence length is fixed while the ambient dimension diverges.<n>We propose a dimension-averaged angular kernel scan framework for detecting marginal distributional shifts.
arXiv Detail & Related papers (2026-05-25T13:45:38Z) - Entropic Reciprocity in Time-Reversed Young Interferometry [0.0]
We show that time-reversed Young interferometry reorganizes, rather than reverses, optical entropy.<n>A fixed detector conditions the reciprocal source--detector Green function and produces a source-label probability distribution.
arXiv Detail & Related papers (2026-05-01T19:31:26Z) - From Random Fringes to Deterministic Response: Statistical Foundations of Time-Reversed Young Interferometry [0.0]
We show that the fringe is not a marginal distribution of detector positions, but a conditional response indexed by a programmed source coordinate.<n>With a fixed detector and a scanned source basis, the observable is an operational hybrid correlator between detector signal and source label.
arXiv Detail & Related papers (2026-04-26T16:40:59Z) - Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks [0.0]
We develop a parametric-based reduced-order modeling framework for parameter-dependent partial differential equations.<n>We derive rigorous error estimates that explicitly quantify the interplay between spatial discretization and parameter approximation.<n>The proposed framework is applied to inverse problems in quantitative photoacoustic tomography, where we derive potential and parameter reconstruction error estimates.
arXiv Detail & Related papers (2026-02-16T14:01:50Z) - Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
arXiv Detail & Related papers (2026-02-04T15:25:02Z) - From Observations to Parameters: Detecting Changepoint in Nonlinear Dynamics with Simulation-based Inference [6.621511814416357]
Param--CPD is a two-stage framework that first amortizes Bayesian inference of governing parameters with a neural posterior estimator trained by simulation-based inference.<n>We show that operating in a physically interpretable parameter space enables accurate and interpretable changepoint detection in nonlinear dynamical systems.
arXiv Detail & Related papers (2025-10-20T15:29:31Z) - Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings.
In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities.
We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
arXiv Detail & Related papers (2024-07-19T13:33:38Z) - Superresolution in separation estimation between two dynamic incoherent sources using spatial demultiplexing [0.0]
Recently, perfect measurement based on spatial mode demultiplexing (SPADE) in Hermite-Gauss modes allowed one to reach the quantum limit of precision for estimation of separation between two weak incoherent stationary sources.
In this paper, we consider another deviation from the perfect setup by discarding the assumption about the stationarity of the sources.
We formulate a measurement algorithm that allows for the reduction of one parameter for estimation in the stationary sources scenario.
arXiv Detail & Related papers (2024-07-15T07:57:57Z) - SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registration [77.13381026159111]
We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration.<n>The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
arXiv Detail & Related papers (2024-05-30T15:55:04Z) - Learning Radio Environments by Differentiable Ray Tracing [56.40113938833999]
We introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns.
We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.
arXiv Detail & Related papers (2023-11-30T13:50:21Z) - Adaptive Annealed Importance Sampling with Constant Rate Progress [68.8204255655161]
Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution.
We propose the Constant Rate AIS algorithm and its efficient implementation for $alpha$-divergences.
arXiv Detail & Related papers (2023-06-27T08:15:28Z) - Optimizing one-axis twists for variational Bayesian quantum metrology [0.0]
In particular, qubit phase estimation, or rotation sensing, appears as a ubiquitous problem with applications to electric field sensing, magnetometry, atomic clocks, and gyroscopes.
We propose a new family of parametrized encoding and decoding protocols called arbitrary-axis twist ansatzes.
We show that it can lead to a substantial reduction in the number of one-axis twists needed to achieve a target estimation error.
arXiv Detail & Related papers (2022-12-23T16:45:15Z) - Spin-squeezing swapping for differential measurements with atom
interferometers and clocks [0.0]
Differential interferometry can be understood as a distributed multisqueeze estimation problem.
We propose to overcome the standard quantum limit by exploiting spin-squeezing in an atomic quantum network.
The scheme is also tested with simulation of noise in atomic clocks and interferometers.
arXiv Detail & Related papers (2022-05-19T17:06:17Z) - Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint
modeling [69.35569554213679]
We show that the Analytic Signal phase, and its gradient have a hitherto unstudied discontinuity in $2-D $ and higher dimensions.
This shortcoming can result in severe artifacts whereas the problem does not exist in $1-D $ signals.
We suggest the use of Linear Symmetry phase, relying on more than one set of Gabor filters, but with a negligible computational add-on.
arXiv Detail & Related papers (2020-05-16T21:17:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.