Pith. sign in

REVIEW 1 cited by

The $O(\alpha^2)$ Initial State QED Corrections to $e^+e^- \rightarrow \gamma^*/Z_0^*$

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2003.14289 v1 pith:HVRERODQ submitted 2020-03-31 hep-ph hep-th

classification hep-phhep-th
keywords correctionsalpharesultstermsbeenciteconfirminitial
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We calculate the complete $O(\alpha^2)$ initial state radiation corrections to $e^+ e^-$ annihilation into a neutral vector boson in a direct analytic computation without any approximation. The corrections are represented in terms of iterated incomplete (elliptic) integrals over alphabets of square--root valued letters. Performing the limit $s \gg m_e^2$, we find discrepancies with the earlier results of Ref.~\cite{Berends:1987ab} and confirm results obtained in Ref.~\cite{Blumlein:2011mi} where the effective method of massive operator matrix elements has been used, which works for all but the power corrections in $m^2_e/s$. In this way, we also confirm the validity of the factorization of massive partons in the Drell--Yan process. We add non--logarithmic terms at $O(\alpha^2)$ which have not been considered in previous calculations. The final results in the limit $s \gg m_e^2$ can be given in terms of Nielsen integrals.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Random Reshuffling-Based Distributed Nash Equilibrium Seeking

    math.OC 2026-04 unverdicted novelty 6.0 of 10

    Random reshuffling yields distributed Nash-seeking algorithms that, under partial decision information, converge linearly to a neighborhood (constant steps) or exactly a.s./in mean square (diminishing steps), outperfo...

Pith tools