scholarly journals Semi-Analytical Monte Carlo Method to Simulate the Signal of the VIP-2 Experiment

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 6
Author(s):  
Edoardo Milotti ◽  
Sergio Bartalucci ◽  
Sergio Bertolucci ◽  
Massimiliano Bazzi ◽  
Mario Bragadireanu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Nuclear Physics ◽  
Current Source ◽  
Detection Sensitivity ◽  
Modulation Scheme ◽  
X Rays ◽  
Monte Carlo Program ◽  
Starting Point ◽  
Potential Improvement ◽  
A Current

The VIP-2 collaboration runs an apparatus in the Gran Sasso underground laboratories of the Italian Institute for Nuclear Physics (INFN) designed to search for anomalous X-rays from electron-atom interactions due to violations of the fundamental antisymmetry of multi-electron wavefunctions. The experiment implements the scheme first proposed by Ramberg and Snow, where a current source injects electrons into a metal strip (the experiment’s target). In this paper we describe the structure of a Monte Carlo program to simulate a new upgrade of the experiment, where the anomalous X-ray emission is modulated by an arbitrary time-varying input current. A novel feature of the simulation algorithm is that the Monte Carlo program is based on a mixture of analytical and numerical methods. We report preliminary, exploratory results on the expected detection rate for different modulations of the injected current; these results are a starting point on the way to optimize the modulation scheme and indicate a large potential improvement of the detection sensitivity.

Secondary Fluorescence Correction for Characteristic and Bremsstrahlung X-Rays Using Monte Carlo X-ray Depth Distributions Applied to Bulk and Multilayer Materials

2019 ◽  
Vol 25 (1) ◽  
pp. 92-104 ◽  
Author(s):  
Yu Yuan ◽  
Hendrix Demers ◽  
Samantha Rudinsky ◽  
Raynald Gauvin
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Hybrid Model ◽  
Analytical Models ◽  
X Rays ◽  
Monte Carlo Program ◽  
X Ray ◽  
Secondary Fluorescence ◽  
Depth Distributions ◽  
Multilayer Materials

AbstractSecondary fluorescence effects are important sources of characteristic X-ray emissions, especially for materials with complicated geometries. Currently, three approaches are used to calculate fluorescence X-ray intensities. One is using Monte Carlo simulations, which are accurate but have drawbacks such as long computation times. The second one is to use analytical models, which are computationally efficient, but limited to specific geometries. The last approach is a hybrid model, which combines Monte Carlo simulations and analytical calculations. In this article, a program is developed by combining Monte Carlo simulations for X-ray depth distributions and an analytical model to calculate the secondary fluorescence. The X-ray depth distribution curves of both the characteristic and bremsstrahlung X-rays obtained from Monte Carlo program MC X-ray allow us to quickly calculate the total fluorescence X-ray intensities. The fluorescence correction program can be applied to both bulk and multilayer materials. Examples for both cases are shown. Simulated results of our program are compared with both experimental data from the literature and simulation data from PENEPMA and DTSA-II. The practical application of the hybrid model is presented by comparing with the complete Monte Carlo program.

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

X rays in Atomic and Nuclear Physics

Physics Today ◽  
1974 ◽  
Vol 27 (7) ◽  
pp. 47-48
Author(s):  
N. A. Dyson ◽  
Winthrop W. Smith
Keyword(s):  
Nuclear Physics ◽  
X Rays

Measured and Monte Carlo simulated surface dose reduction for superficial X-rays incident on tissue with underlying air or bone

2017 ◽  
Vol 45 (2) ◽  
pp. 926-933 ◽  
Author(s):  
John Baines ◽  
Sylwia Zawlodzka ◽  
Tim Markwell ◽  
Millicent Chan
Keyword(s):  
Monte Carlo ◽  
Dose Reduction ◽  
X Rays ◽  
Surface Dose ◽  
Simulated Surface
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