Machine learning at the energy and intensity frontiers of particle physics

被引：300

作者：

Radovic, Alexander ^{[1
]}

Williams, Mike ^{[2
]}

Rousseau, David ^{[3
]}

Kagan, Michael ^{[4
]}

Bonacorsi, Daniele ^{[5
,6
]}

Himmel, Alexander ^{[7
]}

Aurisano, Adam ^{[8
]}

Terao, Kazuhiro ^{[4
]}

Wongjirad, Taritree ^{[9
]}

机构：

[1] Coll William & Mary, Williamsburg, VA 23185 USA

[2] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[3] Univ Paris Saclay, Univ Paris Sud, CNRS, LAL,IN2P3, Orsay, France

[4] SLAC Natl Accelerator Lab, Menlo Pk, CA USA

[5] Univ Bologna, Bologna, Italy

[6] Ist Nazl Fis Nucl, Sez Bologna, Bologna, Italy

[7] Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA

[8] Univ Cincinnati, Cincinnati, OH USA

[9] Tufts Univ, Medford, MA 02155 USA

来源：

NATURE | 2018年 / 560卷 / 7716期

关键词：

ARTIFICIAL NEURAL-NETWORKS;

D O I：

10.1038/s41586-018-0361-2

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

引用

页码：41 / 48

页数：8