相关论文: A new simple PDF parametrization: improved descrip…

We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination…

CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative…

Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared…

We present new parton distribution functions (PDFs) up to next-to-next-to-leading order (NNLO) from the CTEQ-TEA global analysis of quantum chromodynamics. These differ from previous CT PDFs in several respects, including the use of data…

We present a first determination of the nuclear parton distribution functions (nPDF) based on the NNPDF methodology: nNNPDF1.0. This analysis is based on neutral-current deep-inelastic structure function data and is performed up to NNLO in…

A combination is presented of all inclusive deep inelastic cross sections previously published by the H1 and ZEUS collaborations at HERA for neutral and charged current $e^{\pm}p$ scattering for zero beam polarisation. The data were taken…

A short review of the currently available modern parton distribution functions (PDFs)and the theory predictions obtained using those PDFs for several benchmark processes at LHC, including Higgs boson production, is presented in this…

This paper develops a new perspective on parameter-efficient fine-tuning (PEFT) for LLMs, inspired by classical subspace minimization. We introduce a unifying framework, Parameter-Efficient Subspace Optimization (PESO), which recovers…

We study isovector unpolarized and helicity parton distribution functions (PDF) of the proton within the framework of Large Momentum Effective Theory. We use a gauge ensemble, generated by the MILC Collaboration, with a superfine lattice…

Nuclear parton distribution functions (nuclear PDFs) are non-perturbative objects that encode the partonic behaviour of bound nucleons. To avoid potential higher-twist contributions, the data probing the high-$x$ end of nuclear PDFs are…

We review the recent efforts in the NNPDF Collaboration towards a new global extraction of polarized parton distributions functions (pPDF). Polarized PDFs are highly relevant for the interpretation of current and future polarized…

Parton distribution functions (PDFs) describe universal properties of bound states and allow us to calculate scattering amplitudes in processes with large momentum transfer. Calculating PDFs involves the evaluation of matrix elements with a…

We present a global analysis of available data on inclusive structure functions measured in electron-proton scattering at small values of Bjorken-x, including the latest data from the combined HERA analysis on reduced cross sections. Our…

The universality of the large momentum expansion allows computing parton distribution functions (PDFs) starting from any Euclidean correlator with appropriate large momentum Fourier Components. Here we consider current-current correlators…

We study nuclear effects of charged current deep inelastic neutrino-iron scattering in the framework of a chi^2 analysis of parton distribution functions (PDFs). We extract a set of iron PDFs which are used to compute x_Bj-dependent and…

We develop a methodology for the construction of a Hessian representation of Monte Carlo sets of parton distributions, based on the use of a subset of the Monte Carlo PDF replicas as an unbiased linear basis, and of a genetic algorithm for…

Parton distribution functions (PDFs) form an essential part of particle physics calculations. Currently, the most precise predictions for these non-perturbative functions are generated through fits to global data. A problem that several PDF…

Recent advances in large language models are driven by scale, while parameter-efficient fine-tuning (PEFT) enables updating only a small fraction of parameters. Low-Rank Adaptation (LoRA) stores parameter deltas as the product of two small…

We study the correlation between different sets of parton distributions (PDFs). Specifically, viewing different PDF sets as distinct determinations, generally correlated, of the same underlying physical quantity, we examine the extent to…

Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory budgets. Although previous methods improve efficiency through low-rank updates, quantization, or…