Research Article | | Peer-Reviewed

Assessment of Background Radiation Levels on the Lunar Surface and Mapping the Lunar Albedos

Received: 12 August 2024     Accepted: 2 September 2024     Published: 23 September 2024
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Abstract

This literature explores the impact of Galactic Cosmic Radiation (GCR) and Solar Energy Particles (SEP) on lunar surface radiation levels, using data from OLTARIS and CRATER missions. Applying the Focker-Planck equation with Badhwar-O-Neil 2020 constraints, we predict radiation levels for 53 ionic particles. The Ap-8 min model addresses trapped protons and neutron albedo on the lunar regolith. ACE/CRIS’s spectrometer data determines the Isotopic Composition of GCR, generating Linear Energy Transfer (LET) plots. CRATER and OLTARIS data characterize high-energy particles above the lunar surface. A spherical harmonic Lambertian surface is generated, on which contours representing scaled reflectance are obtained by passing the data through a Gaussian kernel. ARIMA and Random Forest machine learning models predict parameters, and HZETRN2020 and OLTARIS data produce an albedo map of the lunar regolith. This research aims to enhance radiation protection strategies for future lunar missions and space exploration. The value of scaled reflectance and radiation plots have been generated to help understand the impact of the predominant 53 ionic particles covering the range from solar activity particles SEP to the galactic radiation GCR. The values are provided by running various stimulations under multiple constraints provided in OLTARIS, and the value of these stimulated results are mapped across the lunar surface ranging from -180 degrees to 180degree by -90degree to 90degree plot, giving an accuracy up to 1895.21 px/m with a resolution of 16 degree per pixel in the generated radiation plot. The radiation flux developed provides a concise and detailed understanding of the nature of radiation entrapment on the lunar regolith. It successfully translates the lunar albedo value as per the scaled reflectance on the surface.

Published in American Journal of Astronomy and Astrophysics (Volume 11, Issue 3)
DOI 10.11648/j.ajaa.20241103.11
Page(s) 65-73
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Lambertian Surface, Scaled Reflectance, Lunar Albedo, SEP, GCR

References
[1] “Radiation Environment on the Moon: Prospects and Challenges for Human Missions” Authors: Wimmer-Schweingruber, R. F., K¨ohler, J., and Zeitlin, C. Journal: Space Science Reviews Publication Year: 2016.
[2] Slaba, T. C. and Whitman, K. (2020a) ‘The Badhwar-o’Neill 2020 GCR model’, Space Weather, 18(6), pp. 1–28.
[3] J. N. Elgin (1984) The Fokker-Planck Equation: Methods of Solution and Applications, Optica Acta: International Journal of Optics, 31:11, 1206-1207,
[4] Calvin, T. and Saganti, P. (2007) ‘Radiation Particle Flux Assessment: Ace/Cris Data’, AIAA SPACE 2007 Conference amp; Exposition [Preprint].
[5] Luo, P. et al. (2022) ‘First measurements of low-energy cosmic rays on the surface of the Lunar Farside from chang’e-4 mission’, Science Advances, 8(2), pp. 1–6.
[6] Ershov, E., Yudina, O., Vinogradova, L., & Shakhanov, N. (2020). EQUIPMENT CONDITION MODELING BASED ON RANDOM FOREST AND ARIMA MACHINE LEARNING ALGORITHM STACKING. Cherepovets State University Bulletin, 4, 32-40.
[7] Wu, Y., & Hapke, B. (2018, February). Spectroscopic observations of the Moon at the lunar surface. Earth and Planetary Science Letters, 484, 145–153.
[8] Gueymard, C. A., Lara-Fanego, V., Sengupta, M., & Xie, Y. (2019, April). Surface albedo and reflectance: Review of definitions, angular and spectral effects, and intercomparison of major data sources in support of advanced solar irradiance modeling over the Americas. Solar Energy, 182, 194–212
[9] Basri, R. and Jacobs, D. W. (2003) ‘Lambertian reflectance and linear subspaces’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), pp. 218–233.
[10] Shahriar Negahdaripour, Chih-Ho Yu ‘A Generalized Brightness Change Model for Computing Optical Flow’,
[11] Henderson-sellers, A., & Hughes, N. (1982). Albedo and its importance in climate theory. Progress in Physical Geography, 6, 1-44.
[12] Grenfell, T., & Maykut, G. (1977). The Optical Properties of Ice and Snow in the Arctic Basin. Journal of Glaciology, 18, 445 - 463.
[13] Nguyen, H., Liu, S., & Do, M. (2013). Subspace methods for computational relighting. 8657.
[14] A. Georghiades, P. Belhumeur, and D. Kriegman, “From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination,” Proc. Int’l Conf. Automatic Face and Gesture Recognition, 2000.
[15] I. Kemelmacher-Shlizerman and R. Basri, "3D Face Reconstruction from a Single Image Using a Single Reference Face Shape," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 394-405, Feb. 2011,
[16] R. Ramamoorthi and P. Hanrahan, “On the Relationship Between Radiance and Irradiance: Determining the Illumination from Images of a Convex Lambertian Object,” J. Optical Soc., vol. 18, no. 10, pp. 2448-2459, 2001.
[17] Sinclair, D. (2000). Smooth region structure: folds, domes, bowls, ridges, valleys and slopes. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), 1, 389-394 vol. 1.
[18] Hagen, H., Schreiber, T., & Gschwind, E. (1990). Methods for surface interrogation. Proceedings of the First IEEE Conference on Visualization: Visualization `90, 187-193.
[19] Holden, H., & LeDrew, E. (1999). Hyperspectral identification of coral reef features. International Journal of Remote Sensing, 20, 2545-2563.
[20] Li, Y., Sun, X., Wang, H., Sun, H., & Li, X. (2012). Automatic Target Detection in High-Resolution Remote Sensing Images Using a Contour-Based Spatial Model. IEEE Geoscience and Remote Sensing Letters, 9, 886-890.
[21] Chung, M. (2020). Gaussian kernel smoothing. ArXiv, abs/2007.09539.
[22] Mukherjee, T., Halder, S., & Sharma, S. Accurate, Comprehensive and Predictive Research on Emissions from an Active Region of Sun and the Effect of the Radiation Topology on the Lower Earth Orbit and the Damage to Human Tissues. (2023). In International Journal of convergence in healthcare (Vol. 3, Issue 2). Pratyaksha Medical Care LLP.
[23] Robert C. Singleterry Jr.; Steve R. Blattnig; Martha S. Clowdsley; Garry D. Qualls; Chris A. Sandridge; Lisa C. Simonsen; Tony C. Slaba; Steven A. Walker; Francis F. Badavi; Jan L. Spangler; Aric R. Aumann; E. Neal Zapp; Robert D. Rutledge; Kerry T. Lee; Ryan B. Norman; John W. Norbury (2011). OLTARIS: On-line tool for the assessment of radiation in space. 68(7-8), 1086–1097.
[24] Gautam D. Badhwar (1997). Supplement: Space Radiation Damage and Biodosimetry || The Radiation Environment in Low-Earth Orbit. Radiation Research, 148(5), S3–S10.
[25] Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32.
[26] Machine Learning Fusion Multi-Source Data Features for Classification Prediction of Lunar Surface Geological Units - W Zuo, X Zeng, X Gao, Z Zhang, D Liu, C Li - Remote Sensing, 2022.
[27] Pieters, C., 120-COLOR LUNAR NIR SPECTROPHOTOMETRY DATA V1.0, MK88-L-120CVF-3-RDR-120COLOR-V1.0, NASA Planetary Data System, 1998
[28] Annunziato, M., & Borzì, A. (2018). A Fokker–Planck control framework for stochastic systems. EMS Surveys in Mathematical Sciences.
[29] Chow, S., Huang, W., Li, Y., & Zhou, H. (2011). Fokker–Planck Equations for a Free Energy Functional or Markov Process on a Graph. Archive for Rational Mechanics and Analysis, 203, 969.
[30] Zaninetti, L. (2020). New Probability Distributions in Astrophysics: IV. The Relativistic Maxwell-Boltzmann Distribution. International Journal of Astronomy and Astrophysics.
[31] Dubinova, A., & Trigger, S. (2011). Advances in the studies of anomalous diffusion in velocity space. arXiv: Statistical Mechanics.
[32] Xue, X., Jin, S., An, F., Zhang, H., Fan, J., Eichhorn, M., Jin, C., Chen, B., Jiang, L., & Yun, T. (2022). Shortwave Radiation Calculation for Forest Plots Using Airborne LiDAR Data and Computer Graphics. Plant Phenomics, 2022.
[33] Hodges, R. (2011). Resolution of the lunar hydrogen enigma. Geophysical Research Letters, 38.
[34] Garg, N., Soni, K., Saxena, T., & Maji, S. (2015). Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution. Noise Control Engineering Journal, 63, 182-194.
[35] N. Darapaneni, D. Reddy, A. R. Paduri, P. Acharya and H. S. Nithin, "Forecasting of COVID-19 in India Using ARIMA Model," 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2020, pp. 0894-0899,
[36] Mondal, Prapanna & Shit, Labani & Goswami, Saptarsi. (2014). Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices. International Journal of Computer Science, Engineering and Applications. 4. 13-29.
[37] Zhang, S., Dai, L., Gao, Y., & Xia, Y. (2020). Adaptive interpolating control for constrained systems with parametric uncertainty and disturbances. International Journal of Robust and Nonlinear Control, 30, 6838-6852.
[38] Jody K. Wilson, Harlan E. Spence, Justin Kasper, Michael Golightly, J. Bern Blake, Joe E. Mazur, Lawrence W. Townsend, Anthony W. Case, Mark Dixon Looper, Cary Zeitlin, Nathan A. Schwadron, “The first cosmic ray albedo proton map of the Moon”, Journal of Geophysical Research,
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  • APA Style

    Halder, S., Kulkarni, A., Thakur, A. (2024). Assessment of Background Radiation Levels on the Lunar Surface and Mapping the Lunar Albedos. American Journal of Astronomy and Astrophysics, 11(3), 65-73. https://doi.org/10.11648/j.ajaa.20241103.11

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    ACS Style

    Halder, S.; Kulkarni, A.; Thakur, A. Assessment of Background Radiation Levels on the Lunar Surface and Mapping the Lunar Albedos. Am. J. Astron. Astrophys. 2024, 11(3), 65-73. doi: 10.11648/j.ajaa.20241103.11

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    AMA Style

    Halder S, Kulkarni A, Thakur A. Assessment of Background Radiation Levels on the Lunar Surface and Mapping the Lunar Albedos. Am J Astron Astrophys. 2024;11(3):65-73. doi: 10.11648/j.ajaa.20241103.11

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  • @article{10.11648/j.ajaa.20241103.11,
      author = {Subhojit Halder and Aarya Kulkarni and Atharva Thakur},
      title = {Assessment of Background Radiation Levels on the Lunar Surface and Mapping the Lunar Albedos
    },
      journal = {American Journal of Astronomy and Astrophysics},
      volume = {11},
      number = {3},
      pages = {65-73},
      doi = {10.11648/j.ajaa.20241103.11},
      url = {https://doi.org/10.11648/j.ajaa.20241103.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajaa.20241103.11},
      abstract = {This literature explores the impact of Galactic Cosmic Radiation (GCR) and Solar Energy Particles (SEP) on lunar surface radiation levels, using data from OLTARIS and CRATER missions. Applying the Focker-Planck equation with Badhwar-O-Neil 2020 constraints, we predict radiation levels for 53 ionic particles. The Ap-8 min model addresses trapped protons and neutron albedo on the lunar regolith. ACE/CRIS’s spectrometer data determines the Isotopic Composition of GCR, generating Linear Energy Transfer (LET) plots. CRATER and OLTARIS data characterize high-energy particles above the lunar surface. A spherical harmonic Lambertian surface is generated, on which contours representing scaled reflectance are obtained by passing the data through a Gaussian kernel. ARIMA and Random Forest machine learning models predict parameters, and HZETRN2020 and OLTARIS data produce an albedo map of the lunar regolith. This research aims to enhance radiation protection strategies for future lunar missions and space exploration. The value of scaled reflectance and radiation plots have been generated to help understand the impact of the predominant 53 ionic particles covering the range from solar activity particles SEP to the galactic radiation GCR. The values are provided by running various stimulations under multiple constraints provided in OLTARIS, and the value of these stimulated results are mapped across the lunar surface ranging from -180 degrees to 180degree by -90degree to 90degree plot, giving an accuracy up to 1895.21 px/m with a resolution of 16 degree per pixel in the generated radiation plot. The radiation flux developed provides a concise and detailed understanding of the nature of radiation entrapment on the lunar regolith. It successfully translates the lunar albedo value as per the scaled reflectance on the surface. 
    },
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Assessment of Background Radiation Levels on the Lunar Surface and Mapping the Lunar Albedos
    
    AU  - Subhojit Halder
    AU  - Aarya Kulkarni
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    DO  - 10.11648/j.ajaa.20241103.11
    T2  - American Journal of Astronomy and Astrophysics
    JF  - American Journal of Astronomy and Astrophysics
    JO  - American Journal of Astronomy and Astrophysics
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    EP  - 73
    PB  - Science Publishing Group
    SN  - 2376-4686
    UR  - https://doi.org/10.11648/j.ajaa.20241103.11
    AB  - This literature explores the impact of Galactic Cosmic Radiation (GCR) and Solar Energy Particles (SEP) on lunar surface radiation levels, using data from OLTARIS and CRATER missions. Applying the Focker-Planck equation with Badhwar-O-Neil 2020 constraints, we predict radiation levels for 53 ionic particles. The Ap-8 min model addresses trapped protons and neutron albedo on the lunar regolith. ACE/CRIS’s spectrometer data determines the Isotopic Composition of GCR, generating Linear Energy Transfer (LET) plots. CRATER and OLTARIS data characterize high-energy particles above the lunar surface. A spherical harmonic Lambertian surface is generated, on which contours representing scaled reflectance are obtained by passing the data through a Gaussian kernel. ARIMA and Random Forest machine learning models predict parameters, and HZETRN2020 and OLTARIS data produce an albedo map of the lunar regolith. This research aims to enhance radiation protection strategies for future lunar missions and space exploration. The value of scaled reflectance and radiation plots have been generated to help understand the impact of the predominant 53 ionic particles covering the range from solar activity particles SEP to the galactic radiation GCR. The values are provided by running various stimulations under multiple constraints provided in OLTARIS, and the value of these stimulated results are mapped across the lunar surface ranging from -180 degrees to 180degree by -90degree to 90degree plot, giving an accuracy up to 1895.21 px/m with a resolution of 16 degree per pixel in the generated radiation plot. The radiation flux developed provides a concise and detailed understanding of the nature of radiation entrapment on the lunar regolith. It successfully translates the lunar albedo value as per the scaled reflectance on the surface. 
    
    VL  - 11
    IS  - 3
    ER  - 

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