A weight Monte Carlo estimation of fluctuations in branching processes

Uchaikin Vladimir () and Kozhemiakina Elena ()
Additional contact information
Uchaikin Vladimir: Department of Theoretical Physics, Ulyanovsk State University, L. Tolstoy str. 42, 432017 Ulyanovsk, Russia
Kozhemiakina Elena: Department of Theoretical Physics, Ulyanovsk State University, L. Tolstoy str. 42, 432017 Ulyanovsk, Russia

Monte Carlo Methods and Applications, 2024, vol. 30, issue 2, 107-129

Abstract: It is well known that shortened modeling of particle trajectories with the use multiplicative statistical weights, as a rule, increases the efficiency of the program (in terms of accuracy/time ratio). This trick is often used in non-branching schemes simulating transfer processes without multiplication (for example, the transfer of X-ray radiation), in which it is sufficient to confine ourselves to studying only the average values of the field characteristics. With an increase in energy, however, multiplication processes begin to play a significant role (the production of electron-photon pairs by gamma quanta with energies above 1.022 MeV, etc.), when the resulting trajectory is not just a broken curve in the phase space, but a branched tree. This technique is also applicable to this process, but only if the study of statistical fluctuations and correlations is not the purpose of the calculation. The present review contains the basic concepts of the Monte Carlo method as applied to the theory of particle transport, demonstration of the weighting method in non-branching processes, and ends with a discussion of unbiased estimates of the second moment and covariance of additive functionals.

Keywords: Random numbers; additive detector; detector response; variance of estimators; simulation by importance (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma-2023-2015 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:30:y:2024:i:2:p:107-129:n:1001

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma-2023-2015

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().