相关论文: Generalized Vector Dominance Model up to 2 GeV
The Gaussian process (GP) regression model is a widely employed surrogate modeling technique for computer experiments, offering precise predictions and statistical inference for the computer simulators that generate experimental data.…
The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be generally improved either by increasing the number of training points or by improving the…
Generalized linear models (GLMs) arise in high-dimensional machine learning, statistics, communications and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing,…
As recently proved in generality by Hedenmalm and Wennman, it is a universal behavior of complex random normal matrix models that one finds a complementary error function behavior at the boundary (also called edge) of the droplet as the…
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein…
The processes of electron-positron annihilation into $\pi^0\gamma$ and into $\pi'(1300)\gamma$ are considered within the NJL model. Intermediate vector mesons $\rho^0$, $\omega$, $\rho'(1450)$, and $\omega'(1420)$ are taken into account.…
Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models. When modeling complex physical systems, both domain…
The cross section of the process $e^+e^-\to\pi^+\pi^-\pi^0$ is measured with a precision of 1.6% to 25% in the energy range between $0.7$ and 3.0 GeV using the Initial State Radiation method. A data set with an integrated luminosity of…
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equation. Bayesian inference for these models has been extensively…
We analyze a 37 pb$^{-1}$ data sample collected with the SND detector at the VEPP-2000 $e^+e^-$ collider in the center-of-mass energy range 1.05--2.00 GeV and present an updated measurement of the $e^+e^- \to \omega \pi^0 \to…
Using a data sample of 6.8 pb$^{-1}$ collected with the CMD-3 detector at the VEPP-2000 $e^+e^-$ collider we select about 2700 events of the $e^+e^- \to p\bar{p}$ process and measure its cross section at 12 energy ponts with about 6\%…
Generalized eigenvalue problems (GEPs) find applications in various fields of science and engineering. For example, principal component analysis, Fisher's discriminant analysis, and canonical correlation analysis are specific instances of…
The $e^+e^- \to \omega \pi^0 \to \pi^0 \pi^0 \gamma$ process was investigated in the SND experiment at the VEPP-2M collider. A narrow energy interval near the $\phi$-meson was scanned. The observed cross-section reveals, at the level of…
High statistics Standard Model processes like fermion- and photon-pair production in e+e- collisions are studied at centre-of-mass energies up to 209 GeV. No significant deviation from the Standard Model is observed, leading to strong…
Although variational autoencoders (VAE) are successfully used to obtain meaningful low-dimensional representations for high-dimensional data, the characterization of critical points of the loss function for general observation models is not…
The cross section for the process $e^+e^- \to \omega\eta$ is measured in the center-of-mass energy range 1.34--2.00 GeV. The analysis is based on data collected with the SND detector at the VEPP-2000 $e^+e^-$ collider. The measured $e^+e^-…
The process e+e- -> gamma gamma (gamma) is studied using data collected by the OPAL detector at LEP between the years 1997 and 2000. The data set corresponds to an integrated luminosity of 672.3pb-1 at centre-of-mass energies lying between…
We study a vector dominance model which predicts a fairly large number of currently interesting decay amplitudes of the types S -> \gamma \gamma, V -> S \gamma and S -> V \gamma, where S and V denote scalar and vector mesons, in terms of…
The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy. In this work, we present a…
Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant…