Searching for exotic particles in high-energy physics with deep learning

被引:739
作者
Baldi, P. [1 ]
Sadowski, P. [1 ]
Whiteson, D. [2 ]
机构
[1] UC Irvine, Dept Comp Sci, Irvine, CA 92617 USA
[2] UC Irvine, Dept Phys & Astron, Irvine, CA 92617 USA
关键词
D O I
10.1038/ncomms5308
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
Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches are often used. Standard approaches have relied on 'shallow' machine-learning models that have a limited capacity to learn complex nonlinear functions of the inputs, and rely on a painstaking search through manually constructed nonlinear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Here, using benchmark data sets, we show that deep-learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8% over the best current approaches. This demonstrates that deep-learning approaches can improve the power of collider searches for exotic particles.
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