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Optomechanical parameter estimation

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Shan Zheng Ang, Glen I. Harris, Warwick P. Bowen, Mankei Tsang

Optics · Submitted 15 Jul 2013

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cram\'er-Rao bound most closely.

how it is stated · asserted 1.00 read the record →

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arXiv ID1307.3800 (version 2)
Pith IDKLW2WWDS, Pith's permanent record of this paper and its review
CitationGlen I. Harris, Mankei Tsang, Shan Zheng Ang, Warwick P. Bowen. "Optomechanical parameter estimation." arXiv:1307.3800 (2013). https://pith.science/paper/KLW2WWDS
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,684 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.38 · randomized assignment 0.01 · states quantitative result 0.48 · compares with prior results 0.95 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cram\'er-Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation-maximization (EM) algorithms in the estimation of the force noise power. When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cram\'er-Rao bound most closely. Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry, and fundamental tests of quantum mechanics.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. contribution 1.00 0.82 0.05 asserted 0.92
s1 The Cram\'er-Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation-maximization (EM) algorithms in the estimation of the force noise power. contribution 0.55 0.92 0.08 asserted 1.00
s2 When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cram\'er-Rao bound most closely. result 1.00 0.92 0.60 asserted 1.00
s3 Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry, and fundamental tests of quantum mechanics. interpretation 0.64 0.21 0.23 hedged possibility 0.68

What kind of paper the classifier reports this is: theoretical result 0.74 · evidence from proof or derivation 0.77.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cram\'er-Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation-maximization (EM) algorithms in the estimation of the force noise power. When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cram\'er-Rao bound most closely. Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry, and fundamental tests of quantum mechanics.

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