Perturbative T-odd Asymmetries in the Drell-Yan Process Revisited

Valery E. Lyubovitskij,Werner Vogelsang,Fabian Wunder,Alexey S. Zhevlakov

PHYSICAL REVIEW D（2024）

Millennium Inst Subatom Phys High Energy Frontier

Cited 3|Views9

Abstract

We calculate the perturbative T-odd contributions to the lepton angular distribution in the Drell-Yan process. Using collinear factorization, we work at the first order in QCD perturbation theory where these contributions appear, O(αs2), and address both W± and γ/Z0 boson exchange. A major focus of our calculation is on the regime where the boson’s transverse momentum QT is much smaller than its mass Q. We carefully expand our results up to next-to-next-to-leading power in QT/Q. Our calculation provides a benchmark for studies of T-odd contributions that employ transverse-momentum dependent parton distribution functions. In the neutral-current case we compare our results for the T-odd structure functions to available ATLAS data. Published by the American Physical Society 2024

Translated text

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper