Katachi (形): Decoding the Imprints of Past Star Formation on Present-day Morphology in Galaxies with Interpretable CNNs

JP Alfonzo, KG Iyer, M Akiyama, GL Bryan… - The Astrophysical …, 2024 - iopscience.iop.org
The physical processes responsible for shaping how galaxies form and quench over time
leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; …

Explaining deep learning of galaxy morphology with saliency mapping

P Bhambra, B Joachimi, O Lahav - Monthly Notices of the Royal …, 2022 - academic.oup.com
We successfully demonstrate the use of explainable artificial intelligence (XAI) techniques
on astronomical data sets in the context of measuring galactic bar lengths. The method …

How to measure galaxy star formation histories. II. Nonparametric models

J Leja, AC Carnall, BD Johnson, C Conroy… - The Astrophysical …, 2019 - iopscience.iop.org
Nonparametric star formation histories (SFHs) have long promised to be the" gold standard"
for galaxy spectral energy distribution (SED) modeling as they are flexible enough to …

A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z∼ 8

C Tohill, SP Bamford, CJ Conselice… - The Astrophysical …, 2024 - iopscience.iop.org
Galaxy morphologies provide valuable insights into their formation processes, tracing the
spatial distribution of ongoing star formation and encoding signatures of dynamical …

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

A Ghosh, CM Urry, A Rau… - The Astrophysical …, 2022 - iopscience.iop.org
We introduce a novel machine-learning framework for estimating the Bayesian posteriors of
morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology …

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

A Ghosh, CM Urry, A Mishra… - The Astrophysical …, 2023 - iopscience.iop.org
Abstract We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to
estimate morphological parameters and associated uncertainties for∼ 8 million galaxies in …

Galaxy morphology network: A convolutional neural network used to study morphology and quenching in∼ 100,000 sdss and∼ 20,000 candels galaxies

A Ghosh, CM Urry, Z Wang, K Schawinski… - The Astrophysical …, 2020 - iopscience.iop.org
We examine morphology-separated color–mass diagrams to study the quenching of star
formation in∼ 100,000 (z∼ 0) Sloan Digital Sky Survey (SDSS) and∼ 20,000 (z∼ 1) …

CANDELS Meets GSWLC: Evolution of the Relationship between Morphology and Star Formation Since z= 2

C Osborne, S Salim, I Damjanov… - The Astrophysical …, 2020 - iopscience.iop.org
Galaxy morphology and its evolution over the cosmic epoch hold important clues for
understanding the regulation of star formation (SF). However, studying the relationship …

A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations

L Zanisi, M Huertas-Company… - Monthly Notices of …, 2021 - academic.oup.com
Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the
physics that shapes galaxies. The agreement between the morphology of simulated and real …

Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

G Martin, S Kaviraj, A Hocking… - Monthly Notices of the …, 2020 - academic.oup.com
Galaxy morphology is a fundamental quantity, which is essential not only for the full
spectrum of galaxy-evolution studies, but also for a plethora of science in observational …