The ALFALFA blind extragalactic survey has populated the faint end of the neutral hydrogen (HI) mass function with statistical confidence for the first time. Of particular interest is a subset of the ALFALFA detections, termed "ultra-compact high-velocity clouds" (UCHVCs). These systems, if located within ~1 Mpc, would populate the lowest-mass end of the HI mass function. Subsequent optical imaging has revealed that some of these UCHVCs harbor associated (though sparse) stellar populations, revealing that they may be some of the most extreme galaxies known in the Local Volume, with optical properties akin to ultra-faint dwarf galaxies but with significant neutral gas reservoirs. In this campaign, we investigate the neutral hydrogen properties of six UCHVC candidate galaxies using deep VLA HI spectral line imaging. A companion poster (Paine et al.) presents details on the data reduction, imaging, and resulting products. Here, we examine the morphological and kinematic properties of selected sources. We apply the modeling software 3D-Barolo to our deep HI images in order to derive the rotation curve and constrain the inclination angle for each source. Successful modeling allows us to determine the dynamical masses of these objects and thus to consider them in the context of various fundamental scaling relations defined by more massive galaxies.
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Examining the Limits of an Artificial Neural Network in Predicting the HI Content of Galaxies
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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- Award ID(s):
- 1637339
- PAR ID:
- 10097647
- Date Published:
- Journal Name:
- American Astronomical Society, AAS Meeting
- Volume:
- 233
- Page Range / eLocation ID:
- 260.01
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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