Exploring QCD matter in extreme conditions with Machine Learning
In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …
problem-solving perspective for physics, offering new avenues for studying strongly …
Machine learning in nuclear physics at low and intermediate energies
Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …
various research fields due to its exciting and powerful capability of modeling tools used for …
Nuclear multifragmentation and phase transition for hot nuclei
B Borderie, MF Rivet - Progress in Particle and Nuclear Physics, 2008 - Elsevier
This review article is focused on the tremendous progress realized during the last fifteen
years in the understanding of multifragmentation and its relationship to the liquid–gas phase …
years in the understanding of multifragmentation and its relationship to the liquid–gas phase …
Deep learning: Extrapolation tool for ab initio nuclear theory
Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been
developed for approximately solving finite nuclei with realistic strong interactions. The …
developed for approximately solving finite nuclei with realistic strong interactions. The …
[HTML][HTML] A fast centrality-meter for heavy-ion collisions at the CBM experiment
A new method of event characterization based on Deep Learning is presented. The PointNet
models can be used for fast, online event-by-event impact parameter determination at the …
models can be used for fast, online event-by-event impact parameter determination at the …
Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
The impact parameter is one of the crucial physical quantities of heavy-ion collisions, and
can affect obviously many observables at the final state, such as the multifragmentation and …
can affect obviously many observables at the final state, such as the multifragmentation and …
Application of machine learning in the determination of impact parameter in the system
Background: Sn 132+ Sn 124 collisions at a beam energy of 270 MeV/nucleon were
performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the …
performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the …
Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
Recently, machine learning (ML) techniques have led to a range of numerous developments
in the field of nuclear and high-energy physics. In heavy-ion collisions, the impact parameter …
in the field of nuclear and high-energy physics. In heavy-ion collisions, the impact parameter …
Systematics of stop** and flow in Au+ Au collisions
A Andronic, J Łukasik, W Reisdorf… - … and Thermodynamics with …, 2006 - Springer
Excitation functions of flow and stop** observables for the Au+ Au system at energies from
40 to 1500 MeV per nucleon are presented. The systematics were obtained by merging the …
40 to 1500 MeV per nucleon are presented. The systematics were obtained by merging the …
Estimation of fusion reaction cross-sections by artificial neural networks
S Akkoyun - Nuclear instruments and methods in physics research …, 2020 - Elsevier
Accurate determination of total fusion and fusion-evaporation reaction cross-sections is an
important task in experimental nuclear physics studies. In this study, we estimated the total …
important task in experimental nuclear physics studies. In this study, we estimated the total …