Testing the DCM-SMM Model on Carbon Fragmentation Data at 300 MeV/nucleon

The DCM-SMM (Dubna Cascade Model–Statistical Multifragmentation Model) model was tested on experimental data on 12C fragmentation at an energy of 300 MeV/nucleon. It is shown that this model describes the yields of light fragments well, which indicates its applicability in the region of lower energies of the scale of several hundred MeV/nucleon.

Authors

Institutions

Publication Details

Journal
Physics of Particles and Nuclei
Published
2026-04-01
DOI
https://doi.org/10.1134/s1063779625701266
Primary Topic
Nuclear physics research studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Testing the DCM-SMM Model on Carbon Fragmentation Data at 300 MeV/nucleon

M. A. Martemianov, I. A. Dukhovskoy, S. A. Bulychjov, A. A. Kulikovskaya et al.
Physics of Particles and Nuclei
Nuclear physics research studies
article

Testing the DCM-SMM Model on Carbon Fragmentation Data at 300 MeV/nucleon

M. A. Martemianov, I. A. Dukhovskoy, S. A. Bulychjov, A. A. Kulikovskaya, B. M. Abramov, V. V. Kulikov, M. A. Matsyuk
article en

Abstract

The DCM-SMM (Dubna Cascade Model–Statistical Multifragmentation Model) model was tested on experimental data on 12C fragmentation at an energy of 300 MeV/nucleon. It is shown that this model describes the yields of light fragments well, which indicates its applicability in the region of lower energies of the scale of several hundred MeV/nucleon.

Physics of Particles and NucleiVol. 57(2)
Moscow Technological Institute (RU), Kurchatov Institute (RU)
Openalex Percentile: Top 6%
Nuclear physics research studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.