arXiv subjects

Huifang Lv

Publications and source records attributed to Huifang Lv.

4 recordsLinked to original sources

arXiv2023

Probing Dark QCD Sector through the Higgs Portal with Machine Learning at the LHC

The QCD-like dark sector with GeV-scale dark hadrons has the potential to generate new signatures at the Large Hadron Collider (LHC). In this paper, we consider a singlet scalar mediator in the tens of GeV-scale that connects the dark sector and the Standard Model (SM) sector via the Higgs portal. We focus on the Higgs-strahlung process, $q\overline{q}'\rightarrow W^{\ast}\rightarrow WH $, to produce a highly boosted Higgs boson. Our scenario predicts two different processes that can generate dark mesons: (1) the cascade decay from the Higgs boson to two light scalar mediators and then to four dark mesons; (2) the Higgs boson decaying to two dark quarks, which then undergo a QCD-like shower and hadronization to produce dark mesons. We apply machine learning techniques, such as Convolutional Neural Network (CNN) and Energy Flow Network (EFN), to the fat jet structure to distinguish these signal processes from large SM backgrounds. We find that the branching ratio of the Higgs boson to two light scalar mediators can be constrained to be less than $10\%$ at 14 TeV LHC with $\mathcal{L} = 3000 fb^{-1}$.

hep-ph↗

arXiv2022

Nested sampling statistical errors

Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling's expression using information theory match the leading terms in Keeton's expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. Whilst intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound.

astro-ph.IM↗

arXiv2022

Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC

Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be around the electroweak scale. In this work, we explore the potential of probing the nearly degenerate light higgsinos with machine learning at the LHC. By analyzing jet images and other jet substructure information, we use the Convolutional Neural Network(CNN) to enhance the signal significance. We find that our deep learning jet image method can improve the previous result based on the conventional cut-flow by about a factor of two at the High-Luminosity LHC.

hep-ph↗

arXiv2022

Global fits of SUSY at future Higgs factories

In this work, we study the impact of electroweak and Higgs precision measurements at future electron-positron colliders on several typical supersymmetric models, including the Constrained Minimal Supersymmetric Standard Model (CMSSM), Non-Universal Higgs Mass generalisations (NUHM1, NUHM2), and the 7-dimensional Minimal Supersymmetric Standard Model (MSSM7). Using publicly-available data from the \textsf{GAMBIT} community, we post-process previous SUSY global fits with additional likelihoods to explore the discovery potential of Higgs factories, such as the Circular Electron Positron Collider (CEPC), the Future Circular Collider (FCC) and the International Linear Collider (ILC). We show that the currently allowed parameter space of these models will be further tested by future precision measurements. In particular, dark matter annihilation mechanisms may be distinguished by precise measurements of Higgs observables.

hep-ph↗