The neutral hydrogen (HI) in galaxies provides the gas reservoir out of which stars are formed. The ability to determine the HI masses for statistically significant samples of galaxies can provide information about the connection between this gas reservoir and the star formation that drives galaxy evolution. However, there are relatively few galaxies for which HI masses are known because these measurements are significantly more difficult to make than optical observations. Artificial neural networks are a type of nonlinear technique that have been used estimate the gas masses from their optical properties (Teimoorinia et al. 2017). We present HI observations of 51 galaxies with gas and stellar properties that are rare in the Arecibo Legacy Fast ALFA Survey (ALFALFA, Haynes et al. 2018) which was used to train the Artificial Neural Network developed by Teimoorinia et al. (ANN, 2017). These sources provide a test of the Artificial Neural Network predictions of HI mass and include some rare and interesting systems including galaxies that are extremely massive in both stellar mass (log M_∗> 11.0) and HI mass (log M_HI> 10.2) with large HI line widths (w_50> 500 km/s). We find that this Artificial Neural Network systematically overestimates the gas fraction of the galaxies in our selected sample, suggesting that care must be taken when using these techniques to predict gas masses for galaxies from a broad range of optical properties.
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Relationships between Stellar Velocity Dispersion and the Atmospheres of Early-type Galaxies
Abstract The Voit et al. black hole feedback valve model predicts relationships between stellar velocity dispersion and atmospheric structure among massive early-type galaxies. In this work, we test that model using the Chandra archival sample of 49 early-type galaxies from Lakhchaura et al. We consider relationships between stellar velocity dispersion and entropy profile slope, multiphase gas extent, and the ratio of cooling time to freefall time. We also define subsamples based on data quality and entropy profile properties that clarify those relationships and enable more specific tests of the model predictions. We find that the atmospheric properties of early-type galaxies generally align with the predictions of the Voit et al. model, in that galaxies with a greater stellar velocity dispersion tend to have radial profiles of pressure, gas density, and entropy with steeper slopes and less extended multiphase gas. Quantitative agreement with the model predictions improves when the sample is restricted to have low central entropy and a stellar velocity dispersion of between 220 and 300 km s −1 .
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- Award ID(s):
- 1714764
- NSF-PAR ID:
- 10383311
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 926
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 181
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-4357/ac4604
