9,688 research outputs found
VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger
We study the benefits of jet- and event-level deep learning methods in
distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs
production at the LHC. We show that a variety of classifiers (CNNs,
attention-based networks) trained on the complete low-level inputs of the full
event achieve significant performance gains over shallow machine learning
methods (BDTs) trained on jet kinematics and jet shapes, and we elucidate the
reasons for these performance gains. Finally, we take initial steps towards the
possibility of a VBF vs. GGF tagger that is agnostic to the Higgs decay mode,
by demonstrating that the performance of our event-level CNN does not change
when the Higgs decay products are removed. These results highlight the
potentially powerful benefits of event-level deep learning at the LHC.Comment: 21 pages+appendices, 16 figures; added references, updated Pythia
shower scheme for VBF, and added Appendix C for version
The semileptonic and radiative decays within the light-cone sum rules
The measured branching ratio of the meson semileptonic decay , which is based on the CLEO data taken at the
peak of resonance, disagrees with the traditional SVZ sum rules
analysis by about three times. In the paper, we show that this discrepancy can
be eliminated by applying the QCD light-cone sum rules (LCSR) approach to
calculate the transition form factors and .
After extrapolating the LCSR predictions of these TFFs to whole -region,
we obtain . Using the CKM matrix
element and the lifetime from the Particle Data Group, we obtain
and , which agree with the CLEO measurements within errors. We
also calculate the branching ratios of the two meson radiative processes
and obtain and , which also agree with the Belle measurements within errors. Thus we
think the LCSR approach is applicable for dealing with the meson decays.Comment: 12 pages, 7 figures, version to be published in EPJ
Positive solution for singular boundary value problems
AbstractA sufficient condition for the existence of positive solutions of the nonlinear boundary value problem uβ³(t) + f(t, u(t)) = 0, 0<t<, uβ²(0) = u(1) = 0is constructed, where f : [0, 1) Γ (0, β) β (0, β) is continuous, f(t, u) is locally Lipschitz continuous, and f(t, u)u is strictly decreasing in u > 0 for each t β (0, 1)
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Domain adaptation problems arise in a variety of applications, where a
training dataset from the \textit{source} domain and a test dataset from the
\textit{target} domain typically follow different distributions. The primary
difficulty in designing effective learning models to solve such problems lies
in how to bridge the gap between the source and target distributions. In this
paper, we provide comprehensive analysis of feature learning algorithms used in
conjunction with linear classifiers for domain adaptation. Our analysis shows
that in order to achieve good adaptation performance, the second moments of the
source domain distribution and target domain distribution should be similar.
Based on our new analysis, a novel extremely easy feature learning algorithm
for domain adaptation is proposed. Furthermore, our algorithm is extended by
leveraging multiple layers, leading to a deep linear model. We evaluate the
effectiveness of the proposed algorithms in terms of domain adaptation tasks on
the Amazon review dataset and the spam dataset from the ECML/PKDD 2006
discovery challenge.Comment: ijca
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