Boosting >H → b(b)over-bar with machine learning

被引：0

作者：

Lin, Joshua ^{[1
,2
]}

Freytsis, Marat ^{[3
]}

Moult, Ian ^{[4
,5
]}

Nachman, Benjamin ^{[1
]}

机构：

[1] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA 94720 USA

[2] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA

[3] Univ Oregon, Inst Theoret Sci, Eugene, OR 97403 USA

[4] Univ Calif Berkeley, Berkeley Ctr Theoret Phys, Berkeley, CA 94720 USA

[5] Lawrence Berkeley Natl Lab, Theoret Phys Grp, Berkeley, CA 94720 USA

来源：

JOURNAL OF HIGH ENERGY PHYSICS | 2018年 / 10期

关键词：

Jets; TRANSVERSE-MOMENTUM;

D O I：

10.1007/JHEP10(2018)101

中图分类号：

O412 [相对论、场论]; O572.2 [粒子物理学];

学科分类号：

摘要：

High-p(T) Higgs production at hadron colliders provides a direct probe of the internal structure of the gg H loop with the channel at the LHC within the realm of possibility. In order to enhance the sensitivity to this process, we develop a two-stream convolutional neural network, with one stream acting on jet information and one using global event properties. The neural network significantly increases the discovery potential of a Higgs signal, both for high-p(T) Standard Model production as well for possible beyond the Standard Model contributions. Unlike most studies for boosted hadronically decaying massive particles, the boosted Higgs search is unique because double b-tagging rejects nearly all background processes that do not have two hard prongs. In this context which goes beyond state-of-the-art two-prong tagging the network is studied to identify the origin of the additional information leading to the increased significance. The procedures described here are also applicable to related final states where they can be used to identify additional sources of discrimination power that are not being exploited by current techniques.

引用

页数：25

共 128 条

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