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Abstract The high incidence rate of the O vi λλ 1032, 1038 absorption around low-redshift, ∼ L * star-forming galaxies has generated interest in studies of the circumgalactic medium. We use the high-resolution EAGLE cosmological simulation to analyze the circumgalactic O vi gas around z ≈ 0.3 star-forming galaxies. Motivated by the limitation that observations do not reveal where the gas lies along the line of sight, we compare the O vi measurements produced by gas within fixed distances around galaxies and by gas selected using line-of-sight velocity cuts commonly adopted by observers. We show that gas selected by a velocity cut of ±300 km s −1 or ±500 km s −1 produces a higher O vi column density, a flatter column density profile, and a higher covering fraction compared to gas within 1, 2, or 3 times the virial radius ( r vir ) of galaxies. The discrepancy increases with impact parameter and worsens for lower-mass galaxies. For example, compared to the gas within 2 r vir , identifying the gas using velocity cuts of 200–500 km s −1 increases the O vi column density by 0.2 dex (0.1 dex) at 1 r vir to over 0.75 dex (0.7 dex) at ≈ 2 r vir for galaxies with stellar masses of 10 9 –10 9.5 M ⊙ (10 10 –10 10.5 M ⊙ ). We furthermore estimate that excluding O vi outside r vir decreases the circumgalactic oxygen mass measured by Tumlinson et al. (2011) by over 50%. Our results demonstrate that gas at large line-of-sight separations but selected by conventional velocity windows has significant effects on the O vi measurements and may not be observationally distinguishable from gas near the galaxies.
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Predicting Stellar Masses of the First Galaxies Using Graph Neural Networks
Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes O(10^6) core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires O(10^3) core-hours foregoing directly simulating internal structure for computational efficiency. In this work, we present a proof-of-concept machine learning regression model, using a graph neural network architecture, to predict the stellar mass of high-redshift galaxies solely from their dark matter merger trees, trained from a radiation hydrodynamics cosmological simulation of the first galaxies.
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- Award ID(s):
- 2108020
- PAR ID:
- 10523637
- Publisher / Repository:
- IOP
- Date Published:
- Journal Name:
- Research Notes of the AAS
- Volume:
- 8
- Issue:
- 4
- ISSN:
- 2515-5172
- Page Range / eLocation ID:
- 108
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3847/2515-5172/ad40ad
