Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies
M Walmsley, C Lintott, T Géron, S Kruk… - Monthly Notices of …, 2022 - academic.oup.com
ABSTRACT We present Galaxy Zoo DECaLS: detailed visual morphological classifications
for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint …
for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint …
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data,
which vary in volume and speed of data processing, from many different instruments that …
which vary in volume and speed of data processing, from many different instruments that …
[HTML][HTML] Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure
Significant investments to upgrade and construct large-scale scientific facilities demand
commensurate investments in R&D to design algorithms and computing approaches to …
commensurate investments in R&D to design algorithms and computing approaches to …
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
We use Bayesian convolutional neural networks and a novel generative model of Galaxy
Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian …
Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian …
FETCH: A deep-learning based classifier for fast transient classification
With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high
candidate rate, usage of machine learning algorithms for candidate classification is a …
candidate rate, usage of machine learning algorithms for candidate classification is a …
Self-supervised representation learning for astronomical images
Sky surveys are the largest data generators in astronomy, making automated tools for
extracting meaningful scientific information an absolute necessity. We show that, without the …
extracting meaningful scientific information an absolute necessity. We show that, without the …
[HTML][HTML] FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles
were proposed in 2016 as prerequisites for proper data management and stewardship, with …
were proposed in 2016 as prerequisites for proper data management and stewardship, with …
Deep learning for pH prediction in water desalination using membrane capacitive deionization
The pH of a solution has a large influence on the ion removal efficiency of the membrane
capacitive deionization (MCDI) process, an electrochemical ion separation process. We …
capacitive deionization (MCDI) process, an electrochemical ion separation process. We …
Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey
We present the results of a proof-of-concept experiment that demonstrates that deep
learning can successfully be used for production-scale classification of compact star clusters …
learning can successfully be used for production-scale classification of compact star clusters …
Unsupervised galaxy morphological visual representation with deep contrastive learning
S Wei, Y Li, W Lu, N Li, B Liang, W Dai… - Publications of the …, 2022 - iopscience.iop.org
Galaxy morphology reflects structural properties that contribute to the understanding of the
formation and evolution of galaxies. Deep convolutional networks have proven to be very …
formation and evolution of galaxies. Deep convolutional networks have proven to be very …