分类： 天文学 >> 天文学 提交时间： 2023-02-19
Aritra Ghosh
C. Megan Urry
Aayush Mishra
Laurence Perreault-Levasseur
Priyamvada Natarajan
David B. Sanders
Daisuke Nagai
Chuan Tian
Nico Cappelluti
Jeyhan S. Kartaltepe
Meredith C. Powell
Amrit Rau
Ezequiel Treister
摘要： We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for $\sim 8$ million galaxies in the Hyper Suprime-Cam (HSC) Wide survey with $z \leq 0.75$ and $m \leq 23$. GaMPEN is a machine learning framework that estimates Bayesian posteriors for a galaxy's bulge-to-total light ratio ($L_B/L_T$), effective radius ($R_e$), and flux ($F$). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with $<1\%$ of our dataset. This two-step process will be critical for applying machine learning algorithms to future large imaging surveys, such as the Rubin-Legacy Survey of Space and Time (LSST), the Nancy Grace Roman Space Telescope (NGRST), and Euclid. By comparing our results to those obtained using light-profile fitting, we demonstrate that GaMPEN's predicted posterior distributions are well-calibrated ($\lesssim 5\%$ deviation) and accurate. This represents a significant improvement over light profile fitting algorithms which underestimate uncertainties by as much as $\sim60\%$. For an overlapping sub-sample, we also compare the derived morphological parameters with values in two external catalogs and find that the results agree within the limits of uncertainties predicted by GaMPEN. This step also permits us to define an empirical relationship between the S\'ersic index and $L_B/L_T$ that can be used to convert between these two parameters. The catalog presented here represents a significant improvement in size ($\sim10 \times $), depth ($\sim4$ magnitudes), and uncertainty quantification over previous state-of-the-art bulge+disk decomposition catalogs. With this work, we also release GaMPEN's source code and trained models, which can be adapted to other datasets.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Alessandro Peca
Nico Cappelluti
Meg Urry
Stephanie LaMassa
Stefano Marchesi
Tonima Ananna
Mislav Baloković
David Sanders
Connor Auge
Ezequiel Treister
Meredith Powell
Tracey Jane Turner
Allison Kirkpatrick
Chuan Tian
摘要： We present X-ray spectral analysis of XMM and Chandra observations in the 31.3 deg$^2$ Stripe-82X (S82X) field. Of the 6181 X-ray sources in this field, we analyze a sample of 2937 active galactic nuclei (AGN) with solid redshifts and sufficient counts determined by simulations. Our results show a population with median values of spectral index $\Gamma=1.94_{-0.39}^{+0.31}$, column density log$\,N_{\mathrm{H}}/\mathrm{cm}^{-2}=20.7_{-0.5}^{+1.2}$ and intrinsic, de-absorbed, 2-10 keV luminosity log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}=44.0_{-1.0}^{+0.7}$, in the redshift range 0-4. We derive the intrinsic fraction of AGN that are obscured ($22\leq\mathrm{log}\,N_{\mathrm{H}}/\mathrm{cm}^{-2}43$. This work constrains the AGN obscuration and spectral shape of the still uncertain high-luminosity and high-redshift regimes (log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}>45.5$, $z>3$), where the obscured AGN fraction rises to $64\pm12\%$. We report a luminosity and density evolution of the X-ray luminosity function, with obscured AGN dominating at all luminosities at $z>2$ and unobscured sources prevailing at log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}>45$ at lower redshifts. Our results agree with evolutionary models in which the bulk of AGN activity is triggered by gas-rich environments and in a downsizing scenario. Also, the black hole accretion density (BHAD) is found to evolve similarly to the star formation rate density, confirming the co-evolution between AGN and host-galaxy, but suggesting different time scales in their growing history. The derived BHAD evolution shows that Compton-thick AGN contribute to the accretion history of AGN as much as all other AGN populations combined.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Chuan Tian
C. Megan Urry
Aritra Ghosh
Ryan Ofman
Tonima Tasnim Ananna
Connor Auge
Nico Cappelluti
Meredith C. Powell
David B. Sanders
Kevin Schawinski
Dominic Stark
Grant R. Tremblay
摘要： We present a machine-learning framework to accurately characterize
morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We
first use PSFGAN to decouple host galaxy light from the central point source,
then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the
host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical
images from five bands of the HSC Wide Survey, we build models independently in
three redshift bins: low $(0
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