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It's a Breeze: The Circumgalactic Medium of a Dwarf Galaxy is Easy to Strip

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abstract

The circumgalactic medium (CGM) of star-forming dwarf galaxies plays a key role in regulating the galactic baryonic cycle. We investigate how susceptible the CGM of dwarf satellite galaxies is to ram pressure stripping (RPS) in Milky Way-like environments. In a suite of hydrodynamical wind tunnel simulations, we model an intermediate-mass dwarf satellite galaxy ($M_{*} = 10^{7.2}~M_{\odot}$) with a multiphase interstellar medium (ISM; $M_{\rm ISM} = 10^{7.9}~M_{\odot}$) and CGM ($M_{\rm CGM,vir} = 10^{8.5}~M_{\odot}$) along two first-infall orbits to more than 500 Myr past pericenter of a Milky Way-like host. The spatial resolution is $\sim$79 pc in the star-forming ISM and $316-632$ pc in the CGM. Our simulations show that the dwarf satellite CGM removal is fast and effective: more than $95\%$ of the CGM mass is ram-pressure-stripped within a few hundred Myrs, even under a weak ram pressure orbit where the ISM stripping is negligible. The conditions for CGM survival are consistent with the analytical halo gas stripping predictions in McCarthy et al. (2008). We also find that including the satellite CGM does not effectively shield its galaxy, and therefore the ISM stripping rate is unaffected. Our results imply that a dwarf galaxy CGM is unlikely to be detected in satellite galaxies; and that the star formation of gaseous dwarf satellites is likely devoid of replenishment from a CGM.

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cs.CV 1

years

2024 1

verdicts

CONDITIONAL 1

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Continuous Concepts Removal in Text-to-image Diffusion Models

cs.CV · 2024-11-30 · conditional · novelty 6.0

CCRT removes multiple concepts from a diffusion model in sequence using a distillation loss and a genetic-algorithm-selected calibration prompt set, preserving text-image alignment better than iterative ESD or fixed multi-concept erasure.

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  • Continuous Concepts Removal in Text-to-image Diffusion Models cs.CV · 2024-11-30 · conditional · none · ref 53 · internal anchor

    CCRT removes multiple concepts from a diffusion model in sequence using a distillation loss and a genetic-algorithm-selected calibration prompt set, preserving text-image alignment better than iterative ESD or fixed multi-concept erasure.