Related papers: Improved phenomenological nuclear charge radius fo…
We study the charge and mass distributions within a nucleon and compute the associated squared radii based on a potential model approach. Different constituent quark configurations such as $\Delta$, $Y$, and quark-diquark are considered and…
Kernel methods are an extremely popular set of techniques used for many important machine learning and data analysis applications. In addition to having good practical performances, these methods are supported by a well-developed theory.…
We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific derivative orders, and…
We prove rates of convergence in the statistical sense for kernel-based least squares regression using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping. This method is directly related…
Random binning features, introduced in the seminal paper of Rahimi and Recht (2007), are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way…
Isotope shifts of the mean square radii (MSR) and electric quadrupole moments of even-even nuclei with 20< Z < 98$ are calculated using a dynamical microscopic model. A single particle Nilsson potential with the Seo set of correction term…
This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the…
The Regge-plus-resonance (RPR) framework for kaon photoproduction on the proton and the neutron is an economical single-channel model with very few parameters. Not only does the RPR model allow one to extract resonance information from the…
We consider learning an unknown target function $f_*$ using kernel ridge regression (KRR) given i.i.d. data $(u_i,y_i)$, $i\leq n$, where $u_i \in U$ is a covariate vector and $y_i = f_* (u_i) +\varepsilon_i \in \mathbb{R}$. A recent string…
The universal radiative corrections common to neutron and super-allowed nuclear beta decays (also known as ``inner'' corrections) are revisited in light of a recent dispersion relation study that found $+2.467(22)\%$, i.e.~about $2.4\sigma$…
We calculate the secular changes of the orbital parameters of a point particle orbiting a Kerr black hole, due to the gravitational radiation reaction. For this purpose, we use the post-Newtonian (PN) approximation in the first order black…
For the last decades, multiple international facilities have developed Radioactive-Ion Beams (RIB) to measure reaction processes including exotic nuclei. These measurements coupled with an accurate theoretical model of the reaction enable…
The main objective of the present work is to correlate quarter-point angle and nuclear radius or nuclear matter distribution. Various phenomenological formulae with parameters for strong absorption radius Rs are obtained and compared by…
The method of random Fourier features (RFF), proposed in a seminal paper by Rahimi and Recht (NIPS'07), is a powerful technique to find approximate low-dimensional representations of points in (high-dimensional) kernel space, for…
Formulas for the combined nuclear-recoil and finite-nuclear-size effects of order $(Z\,\alpha)^5$ and $(Z\,\alpha)^6$ are derived without any expansion in the nuclear charge radius $r_C$, making them applicable to both electronic and muonic…
We study the consistency of the description of charge distributions and radii of nuclear clusters obtained with semiclassical nuclear pasta models. These nuclei are expected to exist in the low density outer crust of neutron stars.…
The rms-radius $R$ of the proton charge distribution is a fundamental quantity needed for precision physics. This radius, traditionally determined from elastic electron-proton scattering via the slope of the Sachs form factor $G_e(q^2)$…
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to the pairwise evaluations in kernel methods, the complexity of kernel computation grows as the data size increases; thus the applicability…
Manifestly Lorentz-invariant baryon chiral perturbation theory is used to calculate the radiative correction of low energy elastic lepton proton scatterings. Corrections of differential cross section and charge asymmetry are given at chiral…
Understanding the spectral properties of kernels offers a principled perspective on generalization and representation quality. While deep models achieve state-of-the-art accuracy in molecular property prediction, kernel methods remain…