Mean Stellar Temperature

This study presents a simplified theoretical model of a main sequence star, assuming uniform density and a state of hydrostatic equilibrium. By integrating the fundamental equation of hydrostatic balance with the ideal gas law, a universal analytical formula is derived for estimating the average internal temperature of such stars. The research provides a mathematical framework to approximate stellar temperatures and explores the functional dependence of temperature on core physical parameters, specifically stellar mass and radius. The results offer a simplified yet robust approach for tracing stellar evolution characteristics in an introductory astrophysical contex

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-19
DOI
https://doi.org/10.5281/zenodo.20111084
Primary Topic
Stellar, planetary, and galactic studies
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Mean Stellar Temperature

Davronbek Sanaev
Zenodo (CERN European Organization for Nuclear Research)
Stellar, planetary, and galactic studies
preprint

Mean Stellar Temperature

Davronbek Sanaev
preprint en

Abstract

This study presents a simplified theoretical model of a main sequence star, assuming uniform density and a state of hydrostatic equilibrium. By integrating the fundamental equation of hydrostatic balance with the ideal gas law, a universal analytical formula is derived for estimating the average internal temperature of such stars. The research provides a mathematical framework to approximate stellar temperatures and explores the functional dependence of temperature on core physical parameters, specifically stellar mass and radius. The results offer a simplified yet robust approach for tracing stellar evolution characteristics in an introductory astrophysical contex

Zenodo (CERN European Organization for Nuclear Research)
International House (PT)
Stellar, planetary, and galactic studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.