On the possibility of obtaining non-diffused proximity functions from cloud-chamber data: I. Fourier deconvolution

Zaider, Marco; Minerbo, Gerald

doi:10.1088/0031-9155/33/11/004

Cited by 2 publications

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“…In a previous paper (Zaider and Minerbo 1988) we examined Fourier deconvolution as a method of obtaining non-diffused proximity functions from cloud-chamber data. In a very general sense, a proximity function can be defined as the probability density of distances between random points uniformly distributed in an object.…”

Section: Introductionmentioning

confidence: 99%

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On the possibility of obtaining non-diffused proximity functions from cloud-chamber data: II. Maximum entropy and Bayesian methods

Zaider

Minerbo²

1988

Phys. Med. Biol.

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Maximum entropy and Bayesian methods are applied to an inversion problem which consists of unfolding diffusion from proximity functions calculated from cloud-chamber data. The solution appears to be relatively insensitive to statistical errors in the data (an important feature) given the limited number of tracks normally available from cloud-chamber measurements. It is the first time, to our knowledge, that such methods are applied to microdosimetry.

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Section: Introductionmentioning

confidence: 99%

“…A derivation of equation (3) and some properties of its solution, t ( U), are discussed in the Appendix. The first paper in this series (Zaider and Minerbo 1988) described a solution of this equation based on Fourier spectral analysis. We examine here a different approach based on maximum entropy (MAXENT) and Bayesian methods.…”

Section: Introductionmentioning

confidence: 99%

On the possibility of obtaining non-diffused proximity functions from cloud-chamber data: II. Maximum entropy and Bayesian methods

Zaider

Minerbo²

1988

Phys. Med. Biol.

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Experimental Calculations of Droplet Diffusion in a Low-Pressure Cloud Chamber

Briden

Holt²,

Simmons³

1994

Radiation Research

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A low-pressure cloud chamber was used for several years to display the tracks created by the passage of ionizing particles through vapors of interest. The spatial distributions of the ions that were formed were of special interest, but the accuracy with which these distributions could be determined was reduced by the presence of diffusion. This meant that the droplets, when photographed, had moved significantly away from the point of creation of the parent ion. In the present investigation photographs obtained by previous workers have been analyzed in an attempt to quantify the extent to which the droplets had diffused. The results suggest that the diffusion, when converted to standard density (1000 kg/m3), was independent of the pressure inside the cloud chamber and the mixture used. It could be represented by a one-dimensional root-mean-square diffusion distance whose value was calculated to be 2.42 +/- 0.04 nm. Values for the diffusion of thermalized electrons (< approximately 4 eV) before capture to form negative ions were also calculated. They appeared to lie in the range 3.5-5.0 nm, and were again independent of the pressure and nature of the mixture. The magnitude of the diffusion was large enough to mask any measurable prediffusion structure for a distance in the region of 10 nm radially around the track path of the alpha-particle and proton tracks analyzed.

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On the possibility of obtaining non-diffused proximity functions from cloud-chamber data: I. Fourier deconvolution

Cited by 2 publications

References 11 publications

On the possibility of obtaining non-diffused proximity functions from cloud-chamber data: II. Maximum entropy and Bayesian methods

On the possibility of obtaining non-diffused proximity functions from cloud-chamber data: II. Maximum entropy and Bayesian methods

Experimental Calculations of Droplet Diffusion in a Low-Pressure Cloud Chamber

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