Improved peak shape fitting in alpha spectra

Pommé, S.; Marroyo, B. Caro

doi:10.1016/j.apradiso.2014.11.023

Cited by 67 publications

(46 citation statements)

References 26 publications

(38 reference statements)

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“…Whereas this function is one of the very best in reproducing measured spectra [41], systematic deviations still remain visible in spectra with very high statistical accuracy (see e.g. [4,41]).…”

Section: Peak Shape Representationmentioning

confidence: 99%

“…[4,41]). Applying other fit functions gives slightly different shapes and therefore also different ratios between fitted peak areas [4].…”

Section: Peak Shape Representationmentioning

confidence: 99%

“…Even more elaborate modeling has recently been implemented, allowing additional convolutions, also at the high-energy side: [41] ∑…”

Section: Peak Shape Representationmentioning

confidence: 99%

“…Various mathematical models have been proposed in the past to faithfully represent the typically asymmetric peak shape induced by mono-energetic alpha particles in detectors (see [41]). One of the most successful analytical models to represent the shape of a single alpha peak is the convolution of an exponential low-energy tail with a Gaussian distribution: [42,43] …”

Section: Peak Shape Representationmentioning

confidence: 99%

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Typical uncertainties in alpha-particle spectrometry

Pommé

2015

Metrologia

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Alpha-particle spectrometry is routinely performed with the aim of measuring absolute activities, activity ratios between different alpha-emitting nuclides or decay data such as branching factors, alpha emission probabilities and relative half-lives. It is most commonly performed with ion-implanted silicon detectors. Strong features of the technique are the low background levels that can be achieved due to low sensitivity to other types of radiation, the intrinsic efficiency close to 1 which reduces the efficiency calculations to a geometrical problem and the uniqueness of the energy spectra for each α-decaying nuclide. The main challenge is the limitation to the attainable energy resolution, even with thin and homogenous sources, which causes alpha energy peaks to be partially unresolved due to their width and low-energy tailing. The spectral deconvolution often requires fitting of analytical functions to each peak in the alpha spectrum. True coincidence effects between alpha particles and subsequently emitted conversion electrons cause distortions of the alpha spectra which lead to significant changes in the apparent peak area ratios. Optimum energy resolution can only be achieved on very thin sources, which puts constraints on the source preparation techniques. Radiochemical separations may be needed to extract the alpha emitters from voluminous matrices and efficiency tracing is performed by adding in another isotope by known amounts. Typical uncertainty components are discussed by means of some hypothetical examples.

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Section: Peak Shape Representationmentioning

confidence: 99%

“…[4,41]). Applying other fit functions gives slightly different shapes and therefore also different ratios between fitted peak areas [4].…”

Section: Peak Shape Representationmentioning

confidence: 99%

“…Even more elaborate modeling has recently been implemented, allowing additional convolutions, also at the high-energy side: [41] ∑…”

Section: Peak Shape Representationmentioning

confidence: 99%

Section: Peak Shape Representationmentioning

confidence: 99%

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Typical uncertainties in alpha-particle spectrometry

Pommé

2015

Metrologia

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“…These iterative algorithms converge to the inverse filtering but they benefit from less suffering from the noise amplification problem. In this category, modelfitting techniques try to describe the measurement by a set of constrained parameters or basis functions and to find the best match [10][11][12]. However, these fitting methods generally lead to unstable solutions [7], which is a well-known problem, in spectrometry for instance [12,13].…”

Section: Introductionmentioning

confidence: 99%

Diffraction effects detection for HDR image-based measurements

Lucat¹,

Hegedüs²,

Pacanowski³

2017

Opt. Express

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Modern imaging techniques have proved to be very efficient to recover a scene with high dynamic range (HDR) values. However, this high dynamic range can introduce star-burst patterns around highlights arising from the diffraction of the camera aperture. The spatial extent of this effect can be very wide and alters pixels values, which, in a measurement context, are not reliable anymore. To address this problem, we introduce a novel algorithm that, utilizing a closed-form PSF, predicts where the diffraction will affect the pixels of an HDR image, making it possible to discard them from the measurement. Our approach gives better results than common deconvolution techniques and the uncertainty values (convolution kernel and noise) of the algorithm output are recovered. References and links1. E. Reinhard, High dynamic range imaging : acquisition, display, and image-based lighting (Morgan Kaufmann/Elsevier, 2010). 2. A. N. A. N. Tikhonov and V.

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Radioactive Noble Gas Detection and Measurement with Plastic Scintillators

Mitev

Cassette

2021

Topics in Applied Physics

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Improved peak shape fitting in alpha spectra

Cited by 67 publications

References 26 publications

Typical uncertainties in alpha-particle spectrometry

Typical uncertainties in alpha-particle spectrometry

Diffraction effects detection for HDR image-based measurements

Radioactive Noble Gas Detection and Measurement with Plastic Scintillators

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