Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant 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 84 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 21 tok/s Pro
GPT-5 High 28 tok/s Pro
GPT-4o 96 tok/s Pro
GPT OSS 120B 462 tok/s Pro
Kimi K2 189 tok/s Pro
2000 character limit reached

Pressure-Regulated, Feedback-Modulated Star Formation In Disk Galaxies (2206.00681v2)

Published 1 Jun 2022 in astro-ph.GA

Abstract: The star formation rate (SFR) in galactic disks depends on both the quantity of available interstellar medium (ISM) gas and its physical state. Conversely, the ISM's physical state depends on the SFR, because the "feedback" energy and momentum injected by recently-formed massive stars is crucial to offsetting losses from turbulent dissipation and radiative cooling. The ISM's physical state also responds to the gravitational field that confines it, with increased weight driving higher pressure. In a quasi-steady state, it is expected that the mean total pressure of different thermal phases will match each other, that the component pressures and total pressure will satisfy thermal and dynamical equilibrium requirements, and that the SFR will adjust as needed to provide the requisite stellar radiation and supernova feedback. The pressure-regulated, feedback-modulated (PRFM) theory of the star-forming ISM formalizes these ideas, leading to a prediction that the SFR per unit area, Sigma_SFR, will scale nearly linearly with ISM weight W. In terms of large-scale gas surface density Sigma, stellar plus dark matter density rho_sd, and effective ISM velocity dispersion sigma_eff, an observable weight estimator is W~P_DE=pi G Sigma2/2+Sigma(2G rho_sd){1/2} sigma_eff, and this is predicted to match the total midplane pressure P_tot. Using a suite of multiphase magnetohydrodynamic simulations run with the TIGRESS computational framework, we test the principles of the PRFM model and calibrate the total feedback yield Upsilon_tot = P_tot/Sigma_SFR ~ 1000 km/s, as well as its components. We compare results from TIGRESS to theory, previous numerical simulations, and observations, finding excellent agreement.

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

Collections

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

Summary

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

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

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