Abstract
The prospects of observing the non-resonant di-Higgs production in the Standard Model at the proposed high energy upgrade of the LHC, viz. the HE-LHC ( sqrt{s} = 27 TeV and ℒ = 15 ab−1) is studied. Various di-Higgs final states are considered based on their cleanliness and production rates. The search for the non-resonant double Higgs production at the HE-LHC is performed in the boverline{b}gamma gamma , boverline{b}{tau}^{+}{tau}^{-} , boverline{b}{WW}^{ast } , WW∗γγ, boverline{b}{ZZ}^{ast } and boverline{b}{mu}^{+}{mu}^{-} channels. The signal-background discrimination is performed through multivariate analyses using the Boosted Decision Tree Decorrelated (BDTD) algorithm in the TMVA framework, the XGBoost toolkit and Deep Neural Network (DNN). The variation in the kinematics of Higgs pair production as a function of the self-coupling of the Higgs boson, λh, is also studied. The ramifications of varying λh on the boverline{b}gamma gamma , boverline{b}{tau}^{+}{tau}^{-} and boverline{b}{WW}^{ast } search analyses optimized for the SM hypothesis is also explored.
Highlights
Zh bbh Zγγ hjj∗Total Signal Significance 0% σsys_unEvent yield after the analysis with XGBoost with 95% probability cut Significance with 5% systematic S/B (× 30) Arbitrary UnitProbability cut on XGBoost outputThe ttprocess is the leading contributor to the background
The combination of search results from multiple final states was observed to yield the most stringent constraints on λh for the case of LHC Run-I and Run-II measurements, as well as the HL-LHC projections, and we aim to explore this facet for the HE-LHC in the present study
The signal and background yields obtained from the Boosted Decision Tree Decorrelated (BDTD) and the XGBoost optimization are listed in table 6 along with the respective signal significance values
Summary
We consider such di-Higgs final states which have photons and/or leptons in the final state, viz. bbγγ, bbτ +τ −, bbW W ∗, W W ∗γγ, bbZZ∗ and bbμ+μ−. We perform a multivariate analysis in the TMVA framework to efficiently discriminate the signal and the background events. In this respect, multifarious kinematic variables are chosen depending upon the di-Higgs final state. The XGBoost training is performed by optimizing the learning rate, max-depth, and the regularization parameters, η and λ. We use a DNN classifier to train the signal and the background events. The performance of the multivariate analyses was optimized to maximize the signal significance while maintaining a reasonably good value of S/B. We discuss the kinematic features of the numerous di-Higgs final states, provide a detailed outline of the analysis strategies and present our results from the detailed collider search. We begin our discussion by studying the features and prospects of the bbγγ channel (which is one of the most promising channels for non-resonant di-Higgs searches at the HL-LHC) in the context of searches at the HE-LHC
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