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Related Concept Videos

NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Related Experiment Video

Updated: Jun 8, 2026

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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Published on: April 12, 2014

Correction for nonlinear photon-counting effects in lidar systems.

D P Donovan, J A Whiteway, A I Carswell

    Applied Optics
    |September 22, 2010
    PubMed
    Summary
    This summary is machine-generated.

    A new analytic model accurately predicts photon-counting (PC) system responses, including nonlinearities from photomultiplier tube (PMT) pulse overlap. This model extends the PC lidar system's dynamic range by one order of magnitude.

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    Last Updated: Jun 8, 2026

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    Published on: January 3, 2016

    Area of Science:

    • Optics and Photonics
    • Instrumentation and Measurement
    • Signal Processing

    Background:

    • Photon-counting (PC) systems are crucial for sensitive light detection.
    • Nonlinearities like count loss and gain in PC systems, caused by overlapping pulses from photomultiplier tubes (PMTs), limit their accuracy and dynamic range.
    • Understanding and modeling these nonlinearities is essential for optimizing PC system performance.

    Purpose of the Study:

    • To develop a useful analytic model for describing the response of a photon-counting (PC) system.
    • To accurately account for nonlinear count loss and apparent count gain due to overlapping PMT pulses.
    • To extend the linear operating range and quantify the nonlinear response of PC systems, thereby increasing their dynamic range.

    Main Methods:

    • Developed an analytic model incorporating PMT pulse amplitude distribution and pulse-height discrimination threshold.
    • Validated the model through comparisons with Monte Carlo simulations.
    • Applied the developed model to a PC lidar system.

    Main Results:

    • The analytic model demonstrated excellent agreement with Monte Carlo simulations.
    • Application to a PC lidar system yielded favorable results.
    • The model successfully quantified the system's response in the nonlinear operating regime.

    Conclusions:

    • The developed analytic model provides an accurate description of PC system response, including nonlinear effects.
    • The model enables accurate quantification of PC system performance, even under nonlinear conditions.
    • The model significantly enhances the useful dynamic range of PC systems, achieving a one-order-of-magnitude increase.