Effective Solid Angle Model and Monte Carlo Method: Improved Estimations to Measure Cosmic Muon Intensity at Sea Level in All Zenith Angles

Abstract

Cosmic muons are highly energetic and penetrative particles and these figures are used for imaging of large and dense objects such as spent nuclear fuels in casks and special nuclear materials in cargo. Cosmic muon intensity depends on the incident angle (zenith angle, φ), and it is known that I(φ) = I0 cos2 φ at sea level. Low intensity of cosmic muon requires long measurement time to acquire statistically meaningful counts. Therefore, high-energy particle simulations e.g., GEANT4, are often used to guide measurement studies. However, the measurable cosmic muon count rate changes upon detector geometry and configuration. Here we develop an “effective solid angle” model to estimate experimental results more accurately than the simple cosine-squared model. We show that the cosine-squared model has large error at high zenith angles (φ ≥ 60°), whereas our model provides improved estimations at all zenith angles. We anticipate our model will enhance the ability to estimate actual measurable cosmic muon count rates in muon imaging applications by reducing the gap between simulation and measurement results. This will increase the value of modeling results and improve the quality of experiments and applications in muon detection and imaging.

A Development of Compact Muon Spectrometer Using Multiple Pressurized Gas Cherenkov Radiators

“…When it comes to muon tomography, or muography, the imaging resolution is often limited by the naturally low muon flux, approximately 7.25 ± 0.1 cm -2 s -1 sr -1 at sea level [25]. In addition, muon flux varies with zenith angle and detector configuration [26,27]. Incorporating additional information such as muon momentum has potential to maximize and expand utilizability of cosmic ray muons to various engineering applications.…”

Optimization-based solution of the optimal range of molecular descriptors for anti-breast cancer medicine candidates

“…The data used in this paper are from the files "Molecular_Descriptor.xlsx", "ERα_activity.xlsx" and "ADMET.xlsx", all from the Chinese National Post-Graduate Mathematic Contest in Modeling 2021 5 . Firstly, according to the files "Molecular_Descriptor.xlsx" and "ERα_activity.xlsx", the gray correlation analysis 6 , Pearson correlation coefficient 7 and cosine similarity [8][9] are jointly used to screen the most significant effective to the bioactivity molecular descriptors. And the regression model between molecular descriptors and biological activity are built using BP neural networks 10 .…”