Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.14619 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911482846904320 |
|---|---|
| author | Chen, Jiunn-Wei Gao, Xiang He, Jinchen Hua, Jun Ji, Xiangdong Schäfer, Andreas Su, Yushan Wang, Wei Yang, Yi-Bo Zhang, Jian-Hui Zhang, Qi-An Zhang, Rui Zhao, Yong |
| author_facet | Chen, Jiunn-Wei Gao, Xiang He, Jinchen Hua, Jun Ji, Xiangdong Schäfer, Andreas Su, Yushan Wang, Wei Yang, Yi-Bo Zhang, Jian-Hui Zhang, Qi-An Zhang, Rui Zhao, Yong |
| contents | Large-Momentum Effective Theory (LaMET) is a physics-guided systematic expansion to calculate light-cone parton distributions, including collinear (PDFs) and transverse-momentum-dependent ones, at any fixed momentum fraction $x$ within a range of $[x_{\rm min}, x_{\rm max}]$. It theoretically solves the ill-posed inverse problem that afflicts other theoretical approaches to collinear PDFs, such as short-distance factorizations. Recently, arXiv:2504.17706 [1] raised practical concerns about whether current or even future lattice data will have sufficient precision in the sub-asymptotic correlation region to support an error-controlled extrapolation -- and if not, whether it becomes an inverse problem where the relevant uncertainties cannot be properly quantified. While we agree that not all current lattice data have the desired precision to qualify for an asymptotic extrapolation, some calculations do, and more are expected in the future. We comment on the analysis and results in Ref. [1] and argue that a physics-based systematic extrapolation still provides the most reliable error estimates, even when the data quality is not ideal. In contrast, re-framing the long-distance asymptotic extrapolation as a data-driven-only inverse problem with ad hoc mathematical conditioning could lead to unnecessarily conservative errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-Momentum Effective Theory's Asymptotic Extrapolation vs the Inverse Problem Chen, Jiunn-Wei Gao, Xiang He, Jinchen Hua, Jun Ji, Xiangdong Schäfer, Andreas Su, Yushan Wang, Wei Yang, Yi-Bo Zhang, Jian-Hui Zhang, Qi-An Zhang, Rui Zhao, Yong High Energy Physics - Lattice High Energy Physics - Phenomenology Nuclear Theory Large-Momentum Effective Theory (LaMET) is a physics-guided systematic expansion to calculate light-cone parton distributions, including collinear (PDFs) and transverse-momentum-dependent ones, at any fixed momentum fraction $x$ within a range of $[x_{\rm min}, x_{\rm max}]$. It theoretically solves the ill-posed inverse problem that afflicts other theoretical approaches to collinear PDFs, such as short-distance factorizations. Recently, arXiv:2504.17706 [1] raised practical concerns about whether current or even future lattice data will have sufficient precision in the sub-asymptotic correlation region to support an error-controlled extrapolation -- and if not, whether it becomes an inverse problem where the relevant uncertainties cannot be properly quantified. While we agree that not all current lattice data have the desired precision to qualify for an asymptotic extrapolation, some calculations do, and more are expected in the future. We comment on the analysis and results in Ref. [1] and argue that a physics-based systematic extrapolation still provides the most reliable error estimates, even when the data quality is not ideal. In contrast, re-framing the long-distance asymptotic extrapolation as a data-driven-only inverse problem with ad hoc mathematical conditioning could lead to unnecessarily conservative errors. |
| title | Large-Momentum Effective Theory's Asymptotic Extrapolation vs the Inverse Problem |
| topic | High Energy Physics - Lattice High Energy Physics - Phenomenology Nuclear Theory |
| url | https://arxiv.org/abs/2505.14619 |