Spectrum unfolding in X-ray spectrometry using the maximum entropy method

Fernández, Jorge Eduardo Chira; Scot, Viviana; Giulio, Eugenio Di

doi:10.1016/j.radphyschem.2012.12.026

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…In the field of radiodiagnostic measurements, Reginatto and Zimbal (2008) have applied Bayesian and Maximum Entropy methods to measurements performed with neutron spectrometers. Fernandez et al (2014) developed the code UMESTRAT (Unfolding Maximum Entropy STRATegy), which applies a semi-automatic strategy to solve the unfolding problem by using a suitable combination of MAXED and GRAVEL for applications in X-ray spectrometry.…”

Section: Introductionmentioning

confidence: 99%

Unfolding X-ray spectra using a flat panel detector. Determination of the accuracy of the method with the Monte Carlo method

Gallardo

Ródenas

Verdú

2019

Radiation Physics and Chemistry

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The primary X-ray spectrum depends on different parameters such as high voltage, filament current, high voltage ripple, anode angle and thickness of filter material. The objective of this work is to determine whether the unfolding technique based on the Tikhonov regularization method is accurate enough to estimate the X-ray spectrum when slight changes in the operation variables are considered. In this frame, several X-ray spectra are considered (extracted from the IPEM78 Catalogue Report) varying the main operation variables of the X-ray tube (high voltage, voltage ripple, filter thickness and filter material). With those spectra, the corresponding absorbed dose curves are obtained by simulation with a MCNP5 model reproducing a flat panel detector and a PMMA wedge. Once the absorbed dose curves are simulated and applying the unfolding Tikhonov regularization method, the unfolded spectrum is obtained, which is finally compared with the theoretical one (IPEM78 Catalogue Report). Discrepancies between unfolded and primary X-ray spectra can be attributed to the fact that this is an ill-posed problem, and the unfolding of the spectrum is strongly affected by the method used to improve the conditioning of the response function (response matrix).

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Section: Introductionmentioning

confidence: 99%

Unfolding X-ray spectra using a flat panel detector. Determination of the accuracy of the method with the Monte Carlo method

Gallardo

Ródenas

Verdú

2019

Radiation Physics and Chemistry

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“…The solution of the unfolding problem is an ever-present issue in X-ray spectrometry. To recover the original spectrum it is necessary to use the detector response function, by solving the so called inverse problem [ 4 ]. There are many different methods that can be used to solve this problem, and each different approach leads to a different unfolding method and a different approximate solution [ 5 ], but in general the strategy is familiar: Search for a solution that is close to a reasonable estimate of the spectrum which could give a good data fit, without over-fitting or under-fitting.…”

Section: Introductionmentioning

confidence: 99%

“…The maximum entropy unfolding technique [ 4 ] solves the inverse problem by imposing a set of physical constraints artificially. The stochastic methods [ 5 ], such as the Monte Carlo methods [ 6 ], Genetic algorithms [ 5 ], and Neural networks [ 7 ], are used to derive the solution spectrum, and are successful in some specific applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing

Xia

Huang

et al. 2018

Sensors

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In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be difficult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements (i.e., compressive sensing), and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of ℓ1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors.

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Low‐E extension of a hand‐held Compton spectrometer

et al. 2019

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The MICOS Compton spectrometer is a tool for the routine spectrometric control of X‐ray generators, mainly in the medical field (<150 kV). The use of a pen‐type NaI (Tl) detector with thin Be window allows to extend its range down to 6 keV, for a better evaluation of the low‐E side of the spectrum, which is determined by filtration. A specific problem is encountered in the deconvolution of the spectrum at very low energies, dominated by the K escape of iodine. The proposed solution combines an approximate step‐by‐step application of the GRAVEL algorithm, whereas the MAXED unfolding software is used for reaching the final solution. A new computer code was developed that combines spectrum acquisition and deconvolution and calculates the main characteristics of the source spectrum: end point energy, mean energy, energy at 50% of the left slope, and air kerma at 1 m from the source.

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Spectrum unfolding in X-ray spectrometry using the maximum entropy method

Cited by 7 publications

References 4 publications

Unfolding X-ray spectra using a flat panel detector. Determination of the accuracy of the method with the Monte Carlo method

Unfolding X-ray spectra using a flat panel detector. Determination of the accuracy of the method with the Monte Carlo method

A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing

Low‐E extension of a hand‐held Compton spectrometer

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