# Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey

arXiv: https://arxiv.org/abs/2501.15739 · HTML: https://mlsanity.com/paper/2501.15739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 67 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)
- [Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data](https://mlsanity.com/paper/1906.11248/) (2019)
- [Using transfer learning to detect galaxy mergers](https://mlsanity.com/paper/1805.10289/) (2018)
- [Using Machine Learning to Determine Morphologies of z<1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey](https://mlsanity.com/paper/2212.09984/) (2022)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Continual Learning for Amortized Bayesian Inference](https://mlsanity.com/paper/2602.22884/) (2026)

## Abstract (arXiv)

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within z<1.4 and m<23 in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low (0<z<0.25), mid (0.25<z<0.5), high (0.5<z<0.9), extra (0.9<z<1.1) and extreme (1.1<z<1.4), and train our models independently in each bin. We use PSFGAN to decompose the AGN point source light from its host galaxy, and invoke the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters of the recovered host galaxy. We first trained our models on simulated data, and then fine-tuned our algorithm via transfer learning using labeled real data. To create training labels for transfer learning, we used GALFIT to fit ~ 20,000 real HSC galaxies in each redshift bin. We comprehensively examined that the predicted values from our final models agree well with the GALFIT values for the vast majority of cases. Our PSFGAN + GaMPEN framework runs at least three orders of magnitude faster than traditional light-profile fitting methods, and can be easily retrained for other morphological parameters or on other datasets with diverse ranges of resolutions, seeing conditions, and signal-to-noise ratios, making it an ideal tool for analyzing AGN host galaxies from large surveys coming soon from the Rubin-LSST, Euclid, and Roman telescopes.