Characterization of nonclassical optical fields by photodetection statistics

Picinbono, B.; Bendjaballah, C.

doi:10.1103/physreva.71.013812

Cited by 19 publications

(31 citation statements)

References 14 publications

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“…As noted in the text, this link between the pair-correlation function and waiting-time distribution functions is due to Picinbono and Bendjaballah (2005), with some additions as found in Larsen (2006). For a statistically homogeneous system with rate , one can use the pair-correlation function to write the probability of observing a particle at time t + t 0 given an observation at t 0 .…”

Section: Appendix B Using Inter-arrival Distributions To Calculate Tmentioning

confidence: 99%

“…The pair-correlation function can be written directly in terms of interarrival time data like that acquired by the instrument. The technique, previously identified by Picinbono and Bendjaballah (2005), was used to compute the estimates of the pair-correlation functions plotted in Fig. 8; the technique is outlined in Appendix B.…”

Section: No Data-set Can Be Justifiably Classified As Perfectly Randomentioning

confidence: 99%

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Spatial distributions of aerosol particles: Investigation of the Poisson assumption

Larsen

2007

Journal of Aerosol Science

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Atmospheric particulates are known to be distributed non-uniformly in space due to spatially localized sources and incomplete mixing. Many studies have extensively examined the variability of aerosol particle concentrations on diurnal, seasonal, and annual timescales. Receiving much less attention, however, are aerosol particle spatial distributions on shorter ( 5 min) temporal scales. Knowledge regarding the spatial distribution on these temporal scales is vital to fully understand and predict the properties of microphysical processes like activation, coagulation, and collision/coalescence, as well as some large-scale phenomena like radiation attenuation through a thick yet dilute layer of particles. Traditional treatments of microphysical phenomena often implicitly assume that particles are perfectly randomly distributed (obey Poisson statistics) on short temporal scales, even though this assumption may be inconsistent with observations made on longer scales. This study seeks to explicitly resolve the statistics of particle positions on a variety of temporal scales (over five orders of magnitude) to determine if and when the assumption of perfect spatial randomness can be justified in nature. Some implications of the results are discussed. ᭧

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Section: Appendix B Using Inter-arrival Distributions To Calculate Tmentioning

confidence: 99%

Section: No Data-set Can Be Justifiably Classified As Perfectly Randomentioning

confidence: 99%

Spatial distributions of aerosol particles: Investigation of the Poisson assumption

Larsen

2007

Journal of Aerosol Science

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“…As a consequence, the expectation value appearing in (1) is the coincidence probability. The function b(t) = c(t)/λ, where λ is the density or the PP, is called the bunching function because it describes the bunching effect appearing in the PP obtained in photodetection [4], [8], [9]. It is also sometimes called the intensity function (see [10, p. 69]).…”

Section: Definition and Measurement Of The Coincidence Functionmentioning

confidence: 99%

“…For example, the structure of the coincidence function is a tool to decide whether an optical field is of classical or of nonclassical nature [8].…”

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Measurements of Second-Order Properties of Point Processes

Picinbono

2008

IEEE Trans. Instrum. Meas.

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Abstract-The second-order statistical properties of point processes (PPs) are described by the coincidence function which can be measured by a coincidence device, but such measurements are long and complicated. We propose another method of measurement, and we analyze its performances. The starting point is that the coincidence function can be deduced from the probability density functions of the life times (the distances between points) of the process. The idea is to transform the PP into a positive signal whose values are these distances. From an appropriate processing of this signal, we deduce the coincidence function. For the validation of the method, we use PPs for which the coincidence function is known. The agreement between theory and experiment is, in general, excellent. Finally, the method is applied to measure the coincidence functions of some PPs for which no theoretical result is available.

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Analyzing cloud droplet spatial tendencies on the millimetre and centimetre scales in stratocumulus clouds

Dodson¹,

Griswold²

2022

Quart J Royal Meteoro Soc

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It is well known that fluid turbulence can affect cloud droplet motion, leading to droplet clustering, which in turn can impact precipitation formation through influences on collision-coalescence. Previous work suggests that droplet clustering, or preferential concentration, of cloud droplets occurs on the order of the Kolmogorov length-scale (𝜂), with the magnitude of this clustering depending on the Stokes number (St). However, the accuracy of these theories remains largely unquantified for in situ atmospheric clouds. Therefore, data gathered from a weakly turbulent marine stratocumulus (Sc) deck during the Variability of the American Monsoons (VAMOS) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) are used to analyze the spatial statistics of cloud droplets by means of one-dimensional pair correlation functions 𝜂(l). Cloud droplet arrival times were recorded onboard the CIRPAS Twin-Otter aircraft over a two-week analysis period using the Artium Flight phase-Doppler Interferometer. Results from 2431 subsets of droplet arrival data indicate that droplet clustering occurs in 95% of cases from analyzing 𝜂(l), with the magnitude of the clustering becoming significant in the turbulence dissipation range, at a length-scale of ∼ 2𝜂. Analyzing 𝜂(l) as a function of in-cloud normalized height (Z * ) indicates that a maximum in the magnitude of average droplet clustering occurs near the Sc middle, at Z * = 0.47. Droplet clustering and St are also found to be strongly correlated at a statistically significant rate.

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Characterization of nonclassical optical fields by photodetection statistics

Cited by 19 publications

References 14 publications

Spatial distributions of aerosol particles: Investigation of the Poisson assumption

Spatial distributions of aerosol particles: Investigation of the Poisson assumption

Measurements of Second-Order Properties of Point Processes

Analyzing cloud droplet spatial tendencies on the millimetre and centimetre scales in stratocumulus clouds

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