希格斯玻色子在電弱對稱作用破缺探索中的角色眾所皆知，而且在極端的相空間中，也利於探索超越標準模型。在強子末態的高逕向動量希格斯粒子的範疇中，我們利用深度學習來研究三個物理現象。

首先，我們在大強子對撞機的環境下，利用強子末態的高逕向動量希格斯粒子模擬數據，來訓練淺層與深層的神經網路結構來當噴流標示器。我們發現利用不同的部分子簇射蒙地卡羅法，都能最優化訓練出來的機器學習模型，這展現出深度學習分類器的普世性。

第二，我們展示了機器學習在辨識高逕向動量希格斯粒子產生途徑的準確性。雙線流卷積神經網絡結合了噴流細部結構資訊與整體事件資訊，為從不同的高逕向動量希格斯粒子產生途徑中，將來自膠子-膠子融合法的事件精確地分離，提供了非常大的探索潛力。

最後，我們研究在14 TeV 高流明大型子對撞機中的希格斯粒子對的產生。我們利用三線流卷積神經網絡，分析在雙希格斯場二重態模型架構中，來自膠子-膠子融合法產生的$pp \to H \to h h \to b\bar bb\bar b$事件。這樣的分析手法可以大幅增進事件訊號與背景訊號的分辨程度，也能提升對於參數空間探測的靈敏度。我們最後展現在雙希格斯場二重態模型架構中，四種類型的分析結果。

我們利用了強子末態的高逕向動量希格斯粒子來當實際的深度學習應用例子，對於的靈敏度探索與增強發現的潛力，在大強子對撞機中或者其他，不論是不是對於高逕向動量希格斯粒子，深度學習也可以非常有彈性的延伸到各種的超越標準模型的課題。

Higgs Bosons are well known to probe the structure of the electroweak-breaking sector and are sensitive probes of physics beyond the Standard Model in extreme regions of phase space. We employ deep learning approach to study phenomenology of boosted Higgs with hadronic final state in three cases.

First, we treat the shallow and deep neural networks as jet tagger for simulated Lorentz boosted Higgs at the LHC. We found that a machine learning model trained on one Parton Shower Monte Carlo and tested on another simulation will likely be optimal. It indicates universality property of deep learning classifier.

Second, we demonstrate the capability of machine learning to identify boosted Higgs production modes accurately. The two-stream convolutional neural network, which combines jet substructure and event information, holds a great discovery potential for boosted Higgs bosons via gluon-gluon fusion by precisely separating events into various production modes.

Finally, we study a Higgs boson pair production at the 14 TeV HL-LHC. We apply three-stream convolutional neural network on the gluon-gluon fusion process $pp \to H \to h h \to b\bar bb\bar b$ in the framework of two-Higgs-doublet models. The approach can substantially improve the signal-background discrimination and thus improve the sensitivity coverage of the relevant parameter space. We show the results for Types I to IV.

We take boosted Higgs in hadronic final state to be concrete examples via deep learning approach. It is flexible and may enhance sensitivity and discovery potential to BSM in a variety of physics processes with and without boosted Higgs bosons at the LHC and beyond.

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