相关论文: Improvement of $q^2$ resolution in semileptonic de…

We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Dumbbell polymer solutions are used as examples to demonstrate the essential ideas. To…

I report results in semileptonic decays of B mesons from the CLEO collaboration, with a focus on recent results. Results for exclusive reconstruction of B -> D* l nu, B -> D l nu and B -> rho l nu are given including the q^2 dependence of…

Heavy-meson semileptonic decays calculations on the lattice are reviewed. The focus is upon obtaining reliable matrix elements. Errors that depend upon the lattice spacing, $a$, are an important source of systematic error. Full $O(a)$…

It has been demonstrated in various contexts that monotonicity leads to better explainability in neural networks. However, not every function can be well approximated by a monotone neural network. We demonstrate that monotonicity can still…

We develop a unified approach for establishing rates of decay for the Fourier transform of a wide class of dynamically defined measures. Among the key features of the method is the systematic use of the $L^2$-flattening theorem obtained in…

A new algorithm is presented to perform the full kinematic reconstruction of top quark pair events produced at future electron-positron colliders in the case of dilepton decays of the $W$ bosons to electrons or muons. The momentum…

Semileptonic decays of heavy baryons consisting of one heavy (Q=b,c) and two light (q=u,d,s) quarks are considered in the heavy-quark--light-diquark approximation. The relativistic quasipotential equation is used for obtaining masses and…

We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models,…

This thesis provides methods and analysis of models which make progress on this goal. The techniques outlined are task agnostic, and should provide benefit when used with nearly any transformer LM. We introduce two new finetuning methods…

Within the framework of the factorization approximation, two body non-leptonic decays of $B$ mesons are related to semileptonic matrix elements and form factors, evaluated with an effective lagrangian incorporating both chiral and heavy…

Deep learning is emerging as a new paradigm for solving inverse imaging problems. However, the deep learning methods often lack the assurance of traditional physics-based methods due to the lack of physical information considerations in…

Inverse problems span across diverse fields. In medical contexts, computed tomography (CT) plays a crucial role in reconstructing a patient's internal structure, presenting challenges due to artifacts caused by inherently ill-posed inverse…

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The…

Tensor decomposition of high-dimensional data often struggles to capture semantically or physically meaningful structures, particularly when relying on reconstruction objectives and fixed-rank constraints. We introduce a no-rank tensor…

For the process of single top quark production within the "simplified model" with a scalar dark matter mediator, a new variable based on angular correlations was presented, for the proper reconstruction of which it is necessary to separate…

Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they…

Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model…

We perform a study of the $B^{(*)}$, $D^{(*)}$ semileptonic decays, using a different method than in conventional approaches, where the matrix elements of the weak operators are evaluated and a detailed spin-angular momentum algebra is…

Kinematic variables designed for pairwise decays to partly undetected final states -- a prominent example being $M_{T2}$ and its Lorentz-invariant version $M_2$ -- have been extensively deployed in high-$p_T$ collider searches. A new range…

We present an improved method of reconstructing New Physics (NP) masses from invariant mass endpoints. While the traditional method focuses on a single NP decay, our method considers the decays of two or more NP particles ($ABC...$) in a…