arXiv subjects

Yi-Lun Chung

Publications and source records attributed to Yi-Lun Chung.

6 recordsLinked to original sources

arXiv2022

Sensitivity on Two-Higgs-Doublet Models from Higgs-Pair Production via $b\bar{b}b\bar{b}$ Final State

Higgs boson pair production is well known to probe the structure of the electroweak symmetry breaking sector. We illustrate using the gluon-fusion process $pp \to H \to h h \to (b\bar b) (b\bar b)$ in the framework of two-Higgs-doublet models and how the machine learning approach (three-stream convolutional neural network) can substantially improve the signal-background discrimination and thus improves the sensitivity coverage of the relevant parameter space. We show that such $gg \to hh \to b \bar b b\bar b$ process can further probe the currently allowed parameter space by HiggsSignals and HiggsBounds at the HL-LHC. The results for Types I to IV are shown.

hep-ph↗

arXiv2022

Exploring the Universality of Hadronic Jet Classification

The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.

hep-ph↗

arXiv2022

Inverting cosmic ray propagation by Convolutional Neural Networks

We propose a machine learning method to investigate the propagation of cosmic rays based on the precisely measured spectra of the primary and secondary cosmic ray nuclei of Li, Be, B, C, and O from AMS-02, ACE, and Voyager-1. We train two convolutional neural networks. One network learns how to infer propagation and source parameters from the energy spectra of cosmic rays, and the other network, which is similar to the former, has the flexibility to learn from the data with added artificial fluctuations. Together with the simulated data generated by GALPROP, we find that both networks can properly invert the propagation process and infer the propagation and source parameters reasonably well. This approach can be much more efficient than the traditional Markov chain Monte Carlo fitting method for deriving the propagation parameters if users choose to update confidence intervals with new experimental data. Both of the trained networks are available at (https://github.com/alan200276/CR_ML).

astro-ph.HE↗

arXiv2021

Disentangling Boosted Higgs Boson Production Modes with Machine Learning

Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.

hep-ph↗

arXiv2020

Sensitivity Reach on the Heavy Neutral Leptons and $τ$-Neutrino Mixing $|U_{τN}|^2 $ at the HL-LHC

The model of heavy neutral leptons (HNLs) is one of the well-motivated models beyond the standard model (BSM) from both theoretical and phenomenological point of views. It is an indispensable ingredient to explain the puzzle of tiny neutrino masses and the origin of the matter-antimatter asymmetry in our Universe, based on the models in which the simplest Type-I seesaw mechanism can be embedded. The HNL with a mass up to the electroweak scale is an attractive scenario which can be readily tested in present or near-future experiments including the LHC. In this work, we study the decay rates of HNLs and find the sensitive parameter space of the mixing angles between the active neutrinos and HNLs. Since the mixing between $ ν_τ $ and HNL is not well established in literature compared with those of $ν_e$ and $ν_μ$ for the HNL of mass in the electroweak scale, we focus on the channel $ pp\rightarrow W^{\pm(\ast)} + X\rightarrow τ^{\pm} N + X$ to search for HNLs at the LHC 14 TeV. The targeted signature consists of three prompt charged leptons, which include at least two tau leptons. After the signal-background analysis, we further set sensitivity bounds on the mixing $ |U_{τN}|^2 $ with $ M_N $ at High-Luminosity LHC (HL-LHC). We predict the testable bounds from HL-LHC can be stronger than the previous LEP constraints and Electroweak Precision Data (EWPD), especially for $ M_N \lesssim $ 50 GeV can reach down to $ |U_{τN}|^2 \approx 2\times 10^{-6} $.

hep-ph↗

arXiv2018

Vector Boson Fusion versus Gluon Fusion

Vector-boson fusion (VBF) is a clean probe of the electroweak-symmetry breaking (EWSB), which inevitably suffers from some level of contamination due to the gluon fusion (ggF). In addition to the jet variables used in the current experimental analysis, we analyze a few more jet-shape variables defined by the girth and integrated jet-shape. Taking $H\to W W^* \to e νμν$ and $H \to γγ$ as examples, we perform the analysis with a new technique of 2-step boosted-decision-tree method, which significantly reduces the contamination of the ggF in the VBF sample, thus, providing a clean environment in probing the EWSB sector.

hep-ph↗