Abstract We measure the CO-to-H2conversion factor (αCO) in 37 galaxies at 2 kpc resolution, using the dust surface density inferred from far-infrared emission as a tracer of the gas surface density and assuming a constant dust-to-metal ratio. In total, we have ∼790 and ∼610 independent measurements ofαCOfor CO (2–1) and (1–0), respectively. The mean values forαCO (2–1)andαCO (1–0)are and , respectively. The CO-intensity-weighted mean is 5.69 forαCO (2–1)and 3.33 forαCO (1–0). We examine howαCOscales with several physical quantities, e.g., the star formation rate (SFR), stellar mass, and dust-mass-weighted average interstellar radiation field strength ( ). Among them, , ΣSFR, and the integrated CO intensity (WCO) have the strongest anticorrelation with spatially resolvedαCO. We provide linear regression results toαCOfor all quantities tested. At galaxy-integrated scales, we observe significant correlations betweenαCOandWCO, metallicity, , and ΣSFR. We also find thatαCOin each galaxy decreases with the stellar mass surface density (Σ⋆) in high-surface-density regions (Σ⋆≥ 100M⊙pc−2), following the power-law relations and . The power-law index is insensitive to the assumed dust-to-metal ratio. We interpret the decrease inαCOwith increasing Σ⋆as a result of higher velocity dispersion compared to isolated, self-gravitating clouds due to the additional gravitational force from stellar sources, which leads to the reduction inαCO. The decrease inαCOat high Σ⋆is important for accurately assessing molecular gas content and star formation efficiency in the centers of galaxies, which bridge “Milky Way–like” to “starburst-like” conversion factors.
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Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection
Abstract We present photometric selection of type 1 quasars in the ≈5.3 deg2XMM-Large Scale Structure survey field with machine learning. We constructed our training and blind-test samples using spectroscopically identified Sloan Digital Sky Survey quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1591 quasars. We assessed the classification performance based on the blind-test sample, and the outcome was favorable, demonstrating high reliability (≈99.9%) and good completeness (≈87.5%). We used XGBoost to estimate photometric redshifts of our selected quasars. The estimated photometric redshifts span a range from 0.41 to 3.75. The outlier fraction of these photometric redshift estimates is ≈17%, and the normalized median absolute deviation (σNMAD) is ≈0.07. To study the quasar disk–corona connection, we constructed a subsample of 1016 quasars with Hyper Suprime-Cami < 22.5 after excluding radio-loud and potentially X-ray-absorbed quasars. The relation between the optical-to-X-ray power-law slope parameter (αOX) and the 2500 Å monochromatic luminosity (L2500Å) for this subsample is with a dispersion of 0.159. We found this correlation in good agreement with the correlations in previous studies. We explored several factors, which may bias theαOX–L2500Årelation, and found that their effects are not significant. We discussed possible evolution of theαOX–L2500Årelation with respect toL2500Åor redshift.
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- Award ID(s):
- 2407089
- PAR ID:
- 10567412
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 979
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 107
- Size(s):
- Article No. 107
- Sponsoring Org:
- National Science Foundation
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