Monte Carlo Simulation of a Silicon Strip Detector Response For Angiography Applications. First approach

2003 ◽  
Author(s):  
C. Ceballos
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Detector Response ◽  
Silicon Strip Detector ◽  
Strip Detector

scholarly journals Monte Carlo simulation for the prediction of precision of absorbance measurements with a miniature CCD spectrometer

2003 ◽  
Vol 25 (2) ◽  
pp. 35-42 ◽  
Author(s):  
E. P. Tsaousoglou ◽  
S. D. Bolis ◽  
C. E. Efstathiou
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Light Intensity ◽  
Wavelength Range ◽  
Low Cost ◽  
Intrinsic Noise ◽  
Array Detector ◽  
Detector Response ◽  
Contour Plots ◽  
Data Acquisition Program

The precision characteristics of the absorbance measurements obtained with a low-cost miniature spectrometer incorporating an array detector were evaluated. Uncertainties in absorbance measurements were due to a combination of non-uniform light intensity and detector response over the wavelength range examined (350-850 nm), in conjunction with the digitization of the intensity indications and the intrinsic noise of the detecting elements. The precision characteristics are presented as contour plots displaying the expected RSD% of absorbances on the absorbance versus wavelength plane. The minimum RSD% for the spectrometer configuration tested was observed within the 0.2-1.5 absorbance units and 500-750 nm wavelength range. Without invoking signal enhancement features of the data-acquisition program (scan average, higher integration times, smoothing based on averaging the signal detected by adjacent pixels), the attainable precision within this range was 0.4-0.8%. A computer program based on Monte Carlo simulations was developed for the prediction of absorbance precision characteristics under various conditions of measurements.

scholarly journals 3D convolutional GAN for fast simulation

2019 ◽  
Vol 214 ◽  
pp. 02010 ◽  
Author(s):  
Sofia Vallecorsa ◽  
Federico Carminati ◽  
Gulrukh Khattak
Keyword(s):  
Machine Learning ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Fast Simulation ◽  
Geant4 Monte Carlo Simulation ◽  
Detector Simulation

Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We describe an R&D activity aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and speed-up standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector and, eventually, the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We describe detailed validation studies comparing our results to Geant4 Monte Carlo simulation. Finally we show how this tool could be generalized to describe a whole class of calorimeters, opening the way to a generic machine learning based fast simulation approach.

NaI(Tl) Detector Response at Different Energies and a Validation with Monte Carlo Simulation

2020 ◽  
pp. 647-655
Author(s):  
Abdelkarim Bazza ◽  
Abdelkader El Hamli ◽  
Mohammed Hamal ◽  
Abdellah Moussa ◽  
Mostapha Zerfaoui ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Detector Response

Application of the Correction Algorithm Based on the ME-LM Method for Improvement of Peak Identification in the Instrumental Spectrum of the Scintillation Gamma-Radiometer

ANRI ◽  
2021 ◽  
Vol 0 (2) ◽  
pp. 54-64
Author(s):  
Aliaksei Zaharadniuk ◽  
Dmitri Abalonski ◽  
Raman Lukashevich
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Processing Time ◽  
Likelihood Estimation ◽  
Detector Response ◽  
Response Matrix ◽  
Correction Algorithm ◽  
Scintillation Gamma

The paper considers an algorithm for correcting the instrumental spectrum of the gamma-radiometer with a NaI(Tl) detector. The algorithm is based on the ME-LM method (Maximum Likelihood Estimation using Expectation Maximization), which uses a detector response matrix obtained by Monte Carlo simulation. The main advantage of the algorithm is the rescaling procedure, which significantly reduces the spectrum processing time.

scholarly journals Simulating a Measurement of the 2nd Knee in the Cosmic Ray Spectrum with an Atmospheric Fluorescence Telescope Tower Array

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Jiali Liu ◽  
Qunyu Yang ◽  
Yunxiang Bai ◽  
Zhen Cao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cosmic Rays ◽  
Event Rate ◽  
Cosmic Ray ◽  
Detector Response ◽  
Simulation Tool ◽  
Trigger System ◽  
Air Shower ◽  
Light Generation

A fluorescence telescope tower array has been designed to measure cosmic rays in the energy range of 1017–1018 eV. A full Monte Carlo simulation, including air shower production, light generation and propagation, detector response, electronics, and trigger system, has been developed for that purpose. Using such a simulation tool, the detector configuration, which includes one main tower array and two side-trigger arrays, 24 telescopes in total, has been optimized. The aperture and the event rate have been estimated. Furthermore, the performance of theXmax⁡technique in measuring composition has also been studied.

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