3 papers
astro-ph.GA2026
VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features
S. Satheesh-Sheeba, P. Sánchez-Sáez, R. J. Assef +39
Photometric redshift estimation for active galactic nuclei (AGNs) remains a fundamental challenge for current and upcoming large-scale photometric surveys. Traditional spectral ene…
astro-ph.GA2026
statmorph-lsst: Quantifying and correcting morphological biases in galaxy surveys
Elizaveta Sazonova, Cameron R. Morgan, Michael Balogh +16
Quantitative morphology provides a key probe of galaxy evolution across cosmic time and environments. However, these metrics can be biased by changes in imaging quality - resolutio…
astro-ph.GA2025
Obscured and unobscured X-ray AGNs I: Host galaxy properties
Carlos G. Bornancini, Gabriel A. Oio, Georgina Coldwell
Active galactic nuclei (AGN) play a crucial role in galaxy evolution by influencing the observational properties of their host galaxies. We investigate the host galaxy properties o…