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Found 8 papers, 6 papers with code
EarthPT: a time series foundation model for Earth Observation
EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind.
Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy.
Realistic galaxy image simulation via score-based generative models
We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies.
Ranked #1 on
Galaxy emergent property recreation
on SDSS Galaxies
Rapid sorting of radio galaxy morphology using Haralick features
We demonstrate the use of Haralick features for the automated classification of radio galaxies.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model
In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system.
Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning
Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
An automatic taxonomy of galaxy morphology using unsupervised machine learning
We then apply the technique to the HST CANDELS fields, creating a catalogue of approximately 60, 000 classifications.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Unsupervised self-organised mapping: a versatile empirical tool for object selection, classification and redshift estimation in large surveys
With a training set of ~3800 galaxies with z_spec<1, we achieve photometric redshift accuracies competitive with other (mainly template fitting) techniques that use a similar number of photometric bands (sigma(Dz)=0. 03 with a ~2% outlier rate when using u*-band to 8um photometry).
Instrumentation and Methods for Astrophysics
