LHC signals of triplet scalars as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks

被引：12

作者：

Dey, Atri ^{[1
]}

Lahiri, Jayita ^{[1
]}

Mukhopadhyaya, Biswarup ^{[2
]}

机构：

[1] HBNI, Harish Chandra Res Inst, Reg Ctr Acceleratorbased Particle Phys, Chhatnag Rd, Allahabad 211019, Uttar Pradesh, India

[2] Indian Inst Sci Educ & Res Kolkata, Dept Phys Sci, Mohanpur 741246, India

来源：

JOURNAL OF HIGH ENERGY PHYSICS | 2020年 / 2020卷 / 06期

关键词：

Beyond Standard Model; Dark matter; Higgs physics; Hadron-Hadron scat; tering (experiments); NEUTRINO MASSES; HIGGS BOSONS; OSCILLATIONS; MIXINGS; SEARCH; MODELS; BOUNDS;

D O I：

10.1007/JHEP06(2020)126

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：

We consider a scenario where an SU(2) triplet scalar acts as the portal for a scalar dark matter particle. We identify regions of the parameter space, where such a triplet coexists with the usual Higgs doublet consistently with all theoretical as well as neutrino, accelerator and dark matter constraints, and the triplet-dominated neutral state has substantial invisible branching fraction. LHC signals are investigated for such regions, in the final statesame-sign dilepton + >= 2 jets +E-T. While straightforward detectability at the high-luminosity run is predicted for some benchmark points in a cut-based analysis, there are other benchmarks where one has to resort to gradient boosting/neural network techniques in order to achieve appreciable signal significance.

引用

页数：37