Self-supervised Representation Learning for Astronomical Images

Identifying strong lenses with unsupervised machine learning using convolutional autoencoder

(2020) Ting-Yun Cheng et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Detecting outliers in astronomical images with deep generative networks

(2020) Berta Margalef-Bentabol et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation

(2020) Ashley Spindler et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Effectively using unsupervised machine learning in next generation astronomical surveys

(2020) I. Reis et al.   Astronomy and Computing

Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey

(2019) Asad Khan et al.   PHYSICS LETTERS B

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

(2019) Mike Walmsley et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

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

(2019) G Martin et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Photometric redshift estimation via deep learning

(2018) A. D’Isanto et al.   ASTRONOMY & ASTROPHYSICS

Improving galaxy morphologies for SDSS with Deep Learning

(2018) H Domínguez Sánchez et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Photometric redshift estimation via deep learning

(2018) A. D’Isanto et al.   ASTRONOMY & ASTROPHYSICS

The many flavours of photometric redshifts

(2018) Mara Salvato et al.   Nature Astronomy

Photometric redshifts from SDSS images using a convolutional neural network

(2018) Johanna Pasquet et al.   ASTRONOMY & ASTROPHYSICS

Transfer learning for galaxy morphology from one survey to another

(2018) H Domínguez Sánchez et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

An automatic taxonomy of galaxy morphology using unsupervised machine learning

(2017) Alex Hocking et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Galaxy Zoo: comparing the demographics of spiral arm number and a new method for correcting redshift bias

(2016) Ross E. Hart et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Photometric redshifts for the SDSS Data Release 12

(2016) Róbert Beck et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Measuring photometric redshifts using galaxy images and Deep Neural Networks

(2016) B. Hoyle   Astronomy and Computing

Rotation-invariant convolutional neural networks for galaxy morphology prediction

(2015) Sander Dieleman et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Morphology in the era of large surveys

(2013) C. Lintott et al.   ASTRONOMY & GEOPHYSICS

Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey

(2013) Kyle W. Willett et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

MEASURING REDDENING WITH SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA AND RECALIBRATING SFD

(2011) Edward F. Schlafly et al.   ASTROPHYSICAL JOURNAL

The Galaxy Zoo survey for giant AGN-ionized clouds: past and present black hole accretion events

(2011) William C. Keel et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Galaxy Zoo: ‘Hanny's Voorwerp’, a quasar light echo?

(2009) Chris J. Lintott et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey★

(2008) Chris J. Lintott et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY