activity
20022026
most citedModified-Entropy Models for the Intracluster Medium

202 citations · 263 across the 28 of their papers we have counts for

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Showing 2024Show all

11 papers · 1 filter

astro-ph.GA2024★ 1 cited

Prevention is better than cure? Feedback from high specific energy winds in cosmological simulations with Arkenstone

Jake S. Bennett, Matthew C. Smith, Drummond B. Fielding +6

We deploy the new Arkenstone galactic wind model in cosmological simulations for the first time, allowing us to robustly resolve the evolution and impact of high specific energy wi…

astro-ph.CO2024

The Effects of AGN Feedback on the Lyman- Forest Flux Power Spectrum

Megan Taylor Tillman, Blakesley Burkhart, Stephanie Tonnesen +2

We study the effects of AGN feedback on the Lyman- forest 1D flux power spectrum (P1D). Using the Simba cosmological-hydrodynamic simulations, we examine the impact that adding…

astro-ph.GA2024

Towards Implementation of the Pressure-Regulated, Feedback-Modulated Model of Star Formation in Cosmological Simulations: Methods and Application to TNG

Sultan Hassan, Eve C. Ostriker, Chang-Goo Kim +12

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable p…

astro-ph.GA2024

Learning the Universe: GalactISM simulations of resolved star formation and galactic outflows across main sequence and quenched galactic environments

Sarah M. R. Jeffreson, Eve C. Ostriker, Chang-Goo Kim +5

We present a suite of six high-resolution chemo-dynamical simulations of isolated galaxies, spanning observed disk-dominated environments on the star-forming main sequence, as well…

astro-ph.GA2024★ 1 cited

Arkenstone -- II. A model for unresolved cool clouds entrained in galactic winds in cosmological simulations

Matthew C. Smith, Drummond B. Fielding, Greg L. Bryan +4

Arkenstone is a new scheme that allows multiphase, stellar feedback-driven winds to be included in coarse resolution cosmological simulations. The evolution of galactic winds and t…

astro-ph.GA2024★ 1 cited

Equilibrium States of Galactic Atmospheres II: Interpretation and Implications

G. M. Voit, C. Carr, D. B. Fielding +5

The scaling of galaxy properties with halo mass suggests that feedback loops regulate star formation, but there is no consensus yet about how those feedback loops work. To help cla…

astro-ph.GA2024★ 1 cited

Equilibrium States of Galactic Atmospheres I: The Flip Side of Mass Loading

G. M. Voit, V. Pandya, D. B. Fielding +5

This paper presents a new framework for understanding the relationship between a galaxy and its circumgalactic medium (CGM). It focuses on how imbalances between heating and coolin…

astro-ph.GA2024★ 1 cited

Metallicity Dependence of Pressure-Regulated Feedback-Modulated Star Formation in the TIGRESS-NCR Simulation Suite

Chang-Goo Kim, Eve C. Ostriker, Jeong-Gyu Kim +8

We present a new simulation suite for the star-forming interstellar medium (ISM) in galactic disks using the TIGRESS-NCR framework. Distinctive aspects of our simulation suite are:…

astro-ph.GA2024

Geometry, Dissipation, Cooling, and the Dynamical Evolution of Wind-Blown Bubbles

Lachlan Lancaster, Eve C. Ostriker, Chang-Goo Kim +2

Bubbles driven by energy and mass injection from small scales are ubiquitous in astrophysical fluid systems and essential to feedback across multiple scales. In particular, O stars…

astro-ph.GA2024

Katachi: Decoding the Imprints of Past Star Formation on Present Day Morphology in Galaxies with Interpretable CNNs

Juan Pablo Alfonzo, Kartheik G. Iyer, Masayuki Akiyama +8

The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; S…

astro-ph.IM2024★ 36 cited

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology

Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12

This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…