Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 67 tok/s
Gemini 2.5 Pro 36 tok/s Pro
GPT-5 Medium 16 tok/s Pro
GPT-5 High 18 tok/s Pro
GPT-4o 66 tok/s Pro
Kimi K2 170 tok/s Pro
GPT OSS 120B 440 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

Distribution of the total angular momentum in relativistic configurations (2107.01129v1)

Published 2 Jul 2021 in physics.atom-ph and physics.plasm-ph

Abstract: This paper is devoted to the analysis of the distribution of the total angular momentum in a relativistic configuration. Using cumulants and generating function formalism this analysis can be reduced to the study of individual subshells with $N$ equivalent electrons of momentum $j$. An expression as a nth-derivative is provided for the generating function of the $J$ distribution and efficient recurrence relations are established. It is shown that this distribution may be represented by a Gram-Charlier-like series which is derived from the corresponding series for the magnetic quantum number distribution. The numerical efficiency of this expansion is fair when the configuration consists of several subshells, while the accuracy is less good when only one subshell is involved. An analytical expression is given for the odd-order momenta while the even-order ones are expressed as a series which provides an acceptable accuracy though being not convergent. Such expressions may be used to obtain approximate values for the number of transitions in a spin-orbit split array: it is shown that the approximation is often efficient when few terms are kept, while some complex cases require to include a large number of terms.

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube