280 research outputs found
Disentangling Boosted Higgs Boson Production Modes with Machine Learning
Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse
momentum () are sensitive probes of physics beyond the Standard Model.
However, high 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.Comment: 17 pages, 9 figure
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.Comment: 25 pages, 7 figures, 7 table
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Liquid biopsy genotyping in lung cancer: ready for clinical utility?
Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy
Sensitivity Reach on the Heavy Neutral Leptons and -Neutrino Mixing 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 and HNL is not well established
in literature compared with those of and for the HNL of
mass in the electroweak scale, we focus on the channel 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 with 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 50 GeV can reach down to .Comment: Published versio
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