PADC-NTM Applied in 7Li+Pb at 31 MeV Reaction Products Study

M.  Cinausero; A. M.  Sajo-Castelli; L.  Sajo-Bohus; J.  Palfalvi; G.  Espinosa

doi:10.15415/jnp.2020.72013

Authors

M. Cinausero National Laboratories of Legnaro, I-35020 Legnaro (Pd), Italy
A. M. Sajo-Castelli Nuclear Physics Laboratory, Simón Bolívar University, Caracas 1080A, Venezuela
L. Sajo-Bohus Nuclear Physics Laboratory, Simón Bolívar University, Caracas 1080A, Venezuela
J. Palfalvi HAS KFKI Atomic Energy Research Institute POB 49, H-1525 Budapest, Hungary
G. Espinosa Institute of Physics, National Autonomous University of Mexico, Coyoacán, México City

DOI:

https://doi.org/10.15415/jnp.2020.72013

Keywords:

Nuclear tracks, PADC, 7Li Pb reaction products identification, 8pLP, Track spectrum unfolding

Abstract

Passive nuclear track methodology (NTM) is applied to study charged particles products of the reaction ⁷Li+Pb at ~ 31 MeV. It is a contribution to the 8pLP Project (LNL-INFN-Italy) in where we show an alternative approach to register charged particle from reaction fragments by PADC detection. The main advantage is that the passive system integrates data over the whole experiment and has its importance for low rate reaction processes. Reaction products as well as scattered beam particles are determined from track shape analysis. Some limitations are inherent to NTM since a priori knowledge is required to correlate track size distribution given by each type of particle emerging from the target. Results show that the passive technique gives useful information when applied in reaction data interpretation for a relatively large range of particle types.

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References

E. Fioretto, IEEE Transactions on Nuclear Science 44, 1017 (1997). https://doi.org/10.1109/23.603796

J. K. Pálfalvi, L. Sajó-Bohus, Solid State Phenomena 238, 16 (2015). https://doi.org/10.4028/www.scientific.net/SSP.238.16

M. Barbui, D. Fabris, S. Moretto, G. Nebbia, P. Nemeth, J.K. Palfalvi, S. Pesente, G. Prete, L. Sajo-Bohus and G. Viesti, Radiation Measurements 44, 857 (2009). https://doi.org/10.1016/j.radmeas.2009.10.048

L. Sajó-Bohus, J.K. Pálfalvi, O. Arevalo, E.D. Greaves, P. Németh, D. Palacios, J. Szabo and I. Eördögh Radiation Measurements 40, 442 (2005). https://doi.org/10.1016/j.radmeas.2005.02.011

B. Dörschel, D. Hermsdorf and K. Kadner, Radiation Measurements 35, 183 (2002). https://doi.org/10.1016/S1350-4487(02)00049-5

J.K. Palfalvi, L. Sajo-Bohus and S. A. Durrani, Radiation Protection Symposium Obergurl/Tyrol 28-30- April, Proceedings 2, 271 (1993).

N. Rozlosnik, L. Sajo-Bohus, C. Birattari, E. Gadioli, L.P. Biró and K. Havancsák, Nanotechnology 8, 32 (1997). https://doi.org/10.1088/0957-4484/8/1/008

Z. Lounis, S. Djeffal, K. Morsli, M. Alla, Nucl. Instr. Meth. Phys. Res. B 179, 543 (2001). https://doi.org/10.1016/S0168-583X(01)00601-2

W. Gonzalez et al., Journal of Nuclear Physics, Material Sciences, Radiation and Applications 2, 83 (2014). https://doi.org/10.15415/jnp.2014.21006

R Development Core Team, 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL. http://www.R-project.org. Accessed August 28, 2019.