Related papers: IR-Improved DGLAP Theory
Deep Learning using the eponymous deep neural networks (DNNs) has become an attractive approach towards various data-based problems of theoretical physics in the past decade. There has been a clear trend to deeper architectures containing…
Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data…
Renormalization group evolution equations describing the scale dependence of quantities in quantum chromodynamics (QCD) play a central role in the interpretation of experimental data. Arguably the most important evolution equations for…
Improved performance in higher-order spectral density estimation is achieved using a general class of infinite-order kernels. These estimates are asymptotically less biased but with the same order of variance as compared to the classical…
I present a calculation of the one particle deep inelastic cross section in the target fragmentation region in (\phi^3)_6. The renormalized cross section gets a large logarithmic correction whose coefficient is precisely the scalar DGLAP…
We review recent progress towards a determination of a set of polarized parton distributions from a global set of deep-inelastic scattering data based on the NNPDF methodology, in analogy with the unpolarized case. This method is designed…
We derive new positivity bounds on spin-dependent parton distributions in multicolored QCD. They are stronger than Soffer inequality. We check that the new inequalities are stable under one-loop DGLAP evolution to higher normalization…
The DGLAP improved color dipole approach provides a good description of data for inclusive direct photon spectra at the energies of RHIC and Tevatron. Within the same framework we predict the transverse momentum distribution of direct…
The modified evolution equation for parton distributions of Dokshitzer, Marchesini and Salam is extended to non-singlet Deep Inelastic Scattering coefficient functions and the physical evolution kernels which govern their scaling violation.…
A consistent factorization theorem is presented in the framework of effective field theories. Conventional factorization suffers from infrared divergences in the soft and collinear parts. We present a factorization theorem in which the…
Empirical "backward" galaxy evolution models for infrared bright galaxies are constrained using multi-band infrared surveys. We developed a new Monte-Carlo algorithm for this task, implementing luminosity dependent distribution functions…
Interest in studying the inclusive photon induced processes of Deep-Inelastic Scattering (DIS) and Diffractive DIS (DDIS) at high energies includes their experimental investigation and thorough theoretical description. The conventional…
We propose a mass-dependent MOM scheme to renormalize UV divergence of unpolarized PDFs at one-loop order. This approach which is based on a once subtracted dispersion relation does not need any regulator. The overall counterterms are…
We claim that factorization implies that the evolution kernel, defined by the logarithmic derivative of the N-th moment of the structure function d ln F_2^N / d ln Q^2, receives logarithmically enhanced contributions (Sudakov logs) from a…
Even for short-distance dominated observables the QCD perturbation expansion is never complete. The divergence of the expansion through infrared renormalons provides formal evidence of this fact. In this article we review how this apparent…
IR microspectroscopy of single biological cells is challenged by strong light scattering, which produces baseline effects and peak distortions in the IR spectra and hinders the direct extraction of chemical information. Current methods for…
Generalised parton distributions are instrumental to study both the three-dimensional structure and the energy-momentum tensor of the nucleon, and motivate numerous experimental programmes involving hard exclusive measurements. Based on a…
There are many award-winning pre-trained Convolutional Neural Network (CNN), which have a common phenomenon of increasing depth in convolutional layers. However, I inspect on VGG network, which is one of the famous model submitted to…
We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion…
Generalized Parton Distributions (GPDs) offer a new way to access the quark and gluon nucleon structure. We advocate the need to supplement the experimental study of deeply virtual Compton scattering by its crossed version, timelike Compton…