Colloquium: Machine learning in nuclear physics

被引：84

作者：

Boehnlein, Amber ^{[1
]}

Diefenthaler, Markus ^{[1
]}

Sato, Nobuo ^{[1
]}

Schram, Malachi ^{[1
]}

Ziegler, Veronique ^{[1
]}

Fanelli, Cristiano ^{[2
,3
]}

Hjorth-Jensen, Morten ^{[4
,5
,6
]}

Horn, Tanja ^{[7
,8
]}

Kuchera, Michelle P. ^{[9
,10
]}

Lee, Dean ^{[4
]}

Nazarewicz, Witold ^{[4
]}

Ostroumov, Peter ^{[4
]}

Orginos, Kostas ^{[1
]}

Poon, Alan ^{[11
]}

Wang, Xin-Nian ^{[11
]}

Scheinker, Alexander ^{[12
]}

Smith, Michael S. ^{[13
]}

Pang, Long-Gang ^{[14
]}

机构：

[1] Thomas Jefferson Natl Accelerator Fac, 12000 Jefferson Ave, Newport News, VA 23606 USA

[2] MIT, Lab Nucl Sci, Cambridge, MA 02139 USA

[3] MIT, Inst Artificial Intelligence & Fundamental Interac, Cambridge, MA 02139 USA

[4] Michigan State Univ, Fac Rare Isotope Beams, E Lansing, MI 48824 USA

[5] Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA

[6] Univ Oslo, Ctr Comp Sci Educ, Dept Phys, N-0316 Oslo, Norway

[7] Catholic Univ Amer, Dept Phys, Washington, DC 20064 USA

[8] Thomas Jefferson Natl Accelerator Fac, 12000 Jefferson Ave, Newport News, VA 23606 USA

[9] Davidson Coll, Dept Phys, Davidson, NC 28035 USA

[10] Davidson Coll, Dept Math & Comp Sci, Davidson, NC 28035 USA

[11] Lawrence Berkeley Natl Lab, Nucl Sci Div, 1 Cyclotron Rd, Berkeley, CA 94720 USA

[12] Accelerator Operat & Technol Div Appl Electrodynam, Los Alamos Natl Lab, Los Alamos, NM 87544 USA

[13] Oak Ridge Natl Lab, Phys Div, Oak Ridge, TN 37831 USA

[14] Cent China Normal Univ, Inst Particle Phys, Key Lab Quark & Lepton Phys, Wuhan 430079, Peoples R China

来源：

REVIEWS OF MODERN PHYSICS | 2022年 / 94卷 / 03期

基金：

美国国家科学基金会;

关键词：

COVARIATE SHIFT; NEURAL-NETWORKS; CLASSIFICATION; IDENTIFICATION; PREDICTION; MODEL;

D O I：

10.1103/RevModPhys.94.031003

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research, which has been transformed by machine learning techniques.

引用

页数：32

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