کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8166585 | 1526238 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Input comparison of radiogenic neutron estimates for ultra-low background experiments
ترجمه فارسی عنوان
مقایسه ورودی برآورد نوترون های رادیوگرافی برای آزمایش های فوق العاده پایین است
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
ابزار دقیق
چکیده انگلیسی
These rare event searches require well-understood and minimized backgrounds. Simulations are used to understand backgrounds caused by naturally occurring radioactivity in the rock and in every piece of shielding and detector material used in these experiments. Most important are processes like spontaneous fission and (α,n) reactions in material close to the detectors that can produce neutrons. A comparison study of the (α,n) reactions between two dedicated software packages is detailed. The cross section libraries, neutron yields, and spectra from the Mei-Zhang-Hime and the SOURCES-4A codes are presented. The resultant yields and spectra are used as inputs to direct dark matter detector toy models in GEANT4, to study the impact of their differences on background estimates and fits. Although differences in neutron yield calculations up to 50% were seen, there was no systematic difference between the Mei-Hime-Zhang and SOURCES-4A results. Neutron propagation simulations smooth differences in spectral shape and yield, and both tools were found to meet the broad requirements of the low-background community.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment - Volume 888, 21 April 2018, Pages 110-118
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment - Volume 888, 21 April 2018, Pages 110-118
نویسندگان
J. Cooley, K.J. Palladino, H. Qiu, M. Selvi, S. Scorza, C. Zhang,