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Abstract
Measuring longitudinally polarized vector boson scattering in the ZZ channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible new physics. We investigated several deep neural network structures and compared their ability to improve the measurement of the longitudinal fraction Z_L Z_L. Using fast simulation with the Delphes framework, a clear improvement is found using a previously investigated 'particle-based' deep neural network on a preprocessed dataset and applying principle component analysis to the outputs.A significance of around 1.7 standard deviations can be achieved with the integrated luminosity of 3000 fb-1 that will be recorded at the High-Luminosity LHC.
Highlights
Vector boson scattering (VBS) is a rare standard model (SM) process which plays a crucial role in electroweak symmetry breaking
Measuring the longitudinally polarized component of VBS is a critical step for the field, as it is closely related to the important theoretical property of unitarity restoration, through Higgs and possible new physics [9,10]
We studied the impact of several data preprocessing methods on the deep neural network (DNN) performance, including standardization (STD), Yeo-Johnson power transformation [20] together with standardization (YJ&STD), and no preprocessing
Summary
Junho Lee ,1 Nicolas Chanon ,2 Andrew Levin, Jing Li, Meng Lu, Qiang Li, and Yajun Mao. Measuring longitudinally polarized vector boson scattering in the ZZ channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible new physics. We investigated several deep neural network structures and compared their ability to improve the measurement of the longitudinal fraction ZLZL. ZZ scattering has recently been observed by ATLAS with a significance larger than 5 standard devpiaffiffitions, using 139 fb−1 of LHC Run II data collected at by CMS with s1⁄4 35.9. A fb−1 collected aptrpioffisrffi measurement made 1⁄4 13 TeV reported an observed significance of 2.7 standard deviations [8]. We compare the performance of several machine-learning models, including a BDT [16]
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