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Title: DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation

Abstract

The modeling of the detectability of special nuclear material (SNM) at ports and border crossings requires accurate knowledge of the background radiation at those locations. Background radiation originates from two main sources, cosmic and terrestrial. Cosmic background is produced by high-energy galactic cosmic rays (GCR) entering the atmosphere and inducing a cascade of particles that eventually impact the earth’s surface. The solar modulation potential represents one of the primary inputs to modeling cosmic background radiation. Usosokin et al. formally define solar modulation potential as “the mean energy loss [per unit charge] of a cosmic ray particle inside the heliosphere…” Modulation potential, a function of elevation, location, and time, shares an inverse relationship with cosmic background radiation. As a result, radiation detector thresholds require adjustment to account for differing background levels, caused partly by differing solar modulations. Failure to do so can result in higher rates of false positives and failed detection of SNM for low and high levels of solar modulation potential, respectively. This study focuses on solar modulation’s time dependence, and seeks the best method to predict modulation for future dates using Python. To address the task of predicting future solar modulation, we utilize both non-linear least squares sinusoidalmore » curve fitting and cubic spline interpolation. This material will be published in transactions of the ANS winter meeting of November, 2016.« less

Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE; U.S. Dept. of Homeland Security (DHS)
OSTI Identifier:
1296694
Report Number(s):
LA-UR-16-26115
TRN: US1601769
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; FORECASTING; HELIOSPHERE; BACKGROUND RADIATION; MODULATION; COSMIC RADIATION; ENERGY LOSSES; LEAST SQUARE FIT; POTENTIALS; COMPUTERIZED SIMULATION; INTERPOLATION; RADIATION DETECTORS; CALIBRATION; NONLINEAR PROBLEMS; TIME DEPENDENCE; DETECTION; FISSILE MATERIALS; SPLINE FUNCTIONS; HARBORS; LEVELS

Citation Formats

Behne, Patrick Alan. DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation. United States: N. p., 2016. Web. doi:10.2172/1296694.
Behne, Patrick Alan. DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation. United States. https://doi.org/10.2172/1296694
Behne, Patrick Alan. 2016. "DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation". United States. https://doi.org/10.2172/1296694. https://www.osti.gov/servlets/purl/1296694.
@article{osti_1296694,
title = {DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation},
author = {Behne, Patrick Alan},
abstractNote = {The modeling of the detectability of special nuclear material (SNM) at ports and border crossings requires accurate knowledge of the background radiation at those locations. Background radiation originates from two main sources, cosmic and terrestrial. Cosmic background is produced by high-energy galactic cosmic rays (GCR) entering the atmosphere and inducing a cascade of particles that eventually impact the earth’s surface. The solar modulation potential represents one of the primary inputs to modeling cosmic background radiation. Usosokin et al. formally define solar modulation potential as “the mean energy loss [per unit charge] of a cosmic ray particle inside the heliosphere…” Modulation potential, a function of elevation, location, and time, shares an inverse relationship with cosmic background radiation. As a result, radiation detector thresholds require adjustment to account for differing background levels, caused partly by differing solar modulations. Failure to do so can result in higher rates of false positives and failed detection of SNM for low and high levels of solar modulation potential, respectively. This study focuses on solar modulation’s time dependence, and seeks the best method to predict modulation for future dates using Python. To address the task of predicting future solar modulation, we utilize both non-linear least squares sinusoidal curve fitting and cubic spline interpolation. This material will be published in transactions of the ANS winter meeting of November, 2016.},
doi = {10.2172/1296694},
url = {https://www.osti.gov/biblio/1296694}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Aug 08 00:00:00 EDT 2016},
month = {Mon Aug 08 00:00:00 EDT 2016}
}