Sample characterization using both neutron and gamma multiplicities

Enqvist, Andreas; Pázsit, Imre; Avdic, Senada

doi:10.1016/j.nima.2010.01.022

Cited by 18 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As is also mentioned in Ref. 7, one possibility is to use machine learning methods similar to those used for the inversion of the joint neutron-gamma measurements, 18 for which training data can be generated by the method demonstrated in this paper. Such a possibility will be explored in future work.…”

Section: Transport Calculation Of Multiplicity Moments For Cylinders • Pázsit and Dykin 13mentioning

confidence: 99%

Transport Calculation of the Multiplicity Moments for Cylinders

Pázsit

Dykin

2021

Nuclear Science and Engineering

Self Cite

View full text Add to dashboard Cite

also referred to as the point model, in the transport model the spatial and angular dependence of the internal fission chain is taken into account with a onespeed transport theory treatment. Quantitative results were given for a spherical item, and the bias of the point model regarding the estimation of the fission rate as compared to the more exact space-dependent model was estimated as a function of the size of the sphere and the α factor.In the present paper the formalism and the quantitative work are extended to the treatment of items with cylindrical shapes, which are more relevant in many practical applications. Results are presented for both square cylinders (D ¼ H) and for tall (H=D > 1) and flat (H=D < 1) cylinders. This way the differences between the cylinder and the sphere on one hand and those between the various cylinder shapes on the other hand can be estimated. The results show that the bias depends on the geometry of the cylinder quite moderately, but similarly to the case of the sphere, the bias of the point model is quite significant for larger item sizes and α values, and it is nonconservative (underestimates the fissile mass) as well.

show abstract

Section: Transport Calculation Of Multiplicity Moments For Cylinders • Pázsit and Dykin 13mentioning

confidence: 99%

Transport Calculation of the Multiplicity Moments for Cylinders

Pázsit

Dykin

2021

Nuclear Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among the detectors that might be used for PSD are liquid scintillators (LSs, such as EJ-309), and plastic scintillators (PSs, organic crystals). Although PSs are the most frequently applied in the field [2][3][4][5][6], they generally show poor performanse on PSD when compared to LSs. As an example, the most recent experimental studies [7] indicate that the new PSs of type EJ-299-33 are inferior to the LSs of type EJ-309 in terms of both detection efficiency of n's, and quality of PSD.…”

Section: Introductionmentioning

confidence: 99%

“…Choosing a suitable number of CVs, as well as appropriately initialising their weights promotes swift LVQ training as well as testing. A variety of methodologies exist to this end, including the one followed here, namely using an approximately equal number of training patterns from each class as initial weights for the codebook vectors 2. The distance expresses the degree of similarity between presented input vector and CVs; a small/large distance corresponds with a high/low degree of similarity, respectively.…”

mentioning

confidence: 99%

Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks

Tambouratzis

Chernikova

Pzsit

2013

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

The potential of two Kohonen artificial neural networks I ANNs) - linear vector quantisa - tion (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n's) and gamma rays (γ’s). The effect that la) the energy level, and lb) the relative- of the training and lest sets, have on iden- tification accuracy is also evaluated on the given PSD datasel The two Kohonen ANNs demonstrate compfcmentary discrimination ability on the training and test sets: while the LVQ is consistently mote accurate on classifying the training set. the SOM exhibits higher n/γ identification rales when classifying new paltms regardless of the proportion of training and test set patterns at the different energy levels: the average tint: for decision making equals ∼100 /e in the cax of the LVQ and ∼450 μs in the case of the SOM.

show abstract

“…For example, the multiperipheral model with a single ladder (Chain, String) is based on the passion distribution for the particle emission centers (resonances, fireballs, clusters, clans, etc.). The multiplicity is sometimes calculated by NNs (Enqvist, Pázsit, and Avdic 2010;El-Bakry, El-Harby, and Behery 2008;El-Harby 2008;Schilders, Meijer, and Ciggaar 2008). Also, an FIS employing fuzzy if-then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

Anfis and Neural Networks Systems for Multiplicity Distributions in Proton-Proton Interactions

Behery

El-Harby

El-Bakry

2013

Applied Artificial Intelligence

View full text Add to dashboard Cite

& This article presents two systems that have the ability to simulate and predict the proton-proton (P-P) interaction. They are an adaptive neurofuzzy inference system (ANFIS) and a neural networks (NNs) system. The P-P-based ANFIS and NNs models calculate the multiplicity distribution of charged particles at different high energies. Simulation results of training charged particles using the ANFIS and NNs as tested with training data points showed perfect fitting to the experimental data. Prediction capabilities of the ANFIS and NNs checked with data points not used in training also proved to perform well. The results amply demonstrate the feasibility of these techniques in extracting the collision features and prove their effectiveness. It is found that the ANFIS shows better performance and trained more quickly than the NNs system.

show abstract

Sample characterization using both neutron and gamma multiplicities

Cited by 18 publications

References 8 publications

Transport Calculation of the Multiplicity Moments for Cylinders

Transport Calculation of the Multiplicity Moments for Cylinders

Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks

Anfis and Neural Networks Systems for Multiplicity Distributions in Proton-Proton Interactions

Contact Info

Product

Resources

About