most citedOn the cosmic evolution of AGN obscuration and the X-ray luminosity function: XMM-Newton and Chandra spectral analysis of the 31.3 deg Stripe 82X

62 citations · 92 across the 7 of their papers we have counts for

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6 papers · 1 filter

astro-ph.GA2026

CHAMPS: The COSMOS High-Redshift ALMA-MIRI Population Survey - A New Census of the Dusty Early Universe

Andreas L. Faisst, Felix Martinez, Jeyhan S. Kartaltepe +86

Understanding how dust forms and evolves over cosmic time is a key open problem in extragalactic astrophysics. Galaxies in the early Universe were once thought to be mostly dust-po…

astro-ph.GA2026

AGN-DB: A Unified Multi-Wavelength Database of Active Galactic Nuclei

Alessandro Peca, Nico Cappelluti, C. Megan Urry +29

We present the Active Galactic Nuclei Database (AGN-DB), a comprehensive, multi-wavelength catalog compiled from more than 100 publicly available AGN catalogs and samples released…

astro-ph.GA2022★ 4 cited

Using Machine Learning to Determine Morphologies of AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

Chuan Tian, C. Megan Urry, Aritra Ghosh +9

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within . We first use PSFGAN to decouple host ga…

astro-ph.GA2022★ 13 cited

Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey

Aritra Ghosh, C. Megan Urry, Aayush Mishra +10

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for million galaxies in the Hyper Supr…

astro-ph.GA2022★ 62 cited

On the cosmic evolution of AGN obscuration and the X-ray luminosity function: XMM-Newton and Chandra spectral analysis of the 31.3 deg Stripe 82X

Alessandro Peca, Nico Cappelluti, Meg Urry +11

We present X-ray spectral analysis of XMM and Chandra observations in the 31.3 deg Stripe-82X (S82X) field. Of the 6181 X-ray sources in this field, we analyze a sample of 2937…

astro-ph.GA2022★ 13 cited

GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters

Aritra Ghosh, C. Megan Urry, Amrit Rau +11

We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology…