Detection, instance segmentation, and classification for astronomical surveys with deep learning (deepdisc): detectron2 implementation and demonstration with …
The next generation of wide-field deep astronomical surveys will deliver unprecedented
amounts of images through the 2020s and beyond. As both the sensitivity and depth of …
amounts of images through the 2020s and beyond. As both the sensitivity and depth of …
Denoising diffusion probabilistic models to predict the density of molecular clouds
We introduce the state-of-the-art deep-learning denoising diffusion probabilistic model as a
method to infer the volume or number density of giant molecular clouds (GMCs) from …
method to infer the volume or number density of giant molecular clouds (GMCs) from …
Detection of stellar wakes in the Milky Way: A deep learning approach
Context. Due to poor observational constraints on the low-mass end of the subhalo mass
function, the detection of dark matter (DM) subhalos on sub-galactic scales would provide …
function, the detection of dark matter (DM) subhalos on sub-galactic scales would provide …
Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
X Zhou, Y Gong, X Zhang, N Li, XM Meng… - The Astrophysical …, 2024 - iopscience.iop.org
Abstract Chinese Space Station Telescope (CSST) has the capability to conduct a slitless
spectroscopic survey simultaneously with a photometric survey. The spectroscopic survey …
spectroscopic survey simultaneously with a photometric survey. The spectroscopic survey …
Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
A Hsu, M Ho, J Lin, C Markey, M Ntampaka… - arXiv preprint arXiv …, 2024 - arxiv.org
We present a novel approach to reconstruct gas and dark matter projected density maps of
galaxy clusters using score-based generative modeling. Our diffusion model takes in mock …
galaxy clusters using score-based generative modeling. Our diffusion model takes in mock …
A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program
The conditions under which galactic nuclear regions become active are largely unknown,
although it has been hypothesized that secular processes related to galaxy morphology …
although it has been hypothesized that secular processes related to galaxy morphology …
Score-matching neural networks for improved multi-band source separation
ML Sampson, P Melchior, C Ward… - arXiv preprint arXiv …, 2024 - arxiv.org
We present the implementation of a score-matching neural network that represents a data-
driven prior for non-parametric galaxy morphologies. The gradients of this prior can be …
driven prior for non-parametric galaxy morphologies. The gradients of this prior can be …
Denoising Diffusion Probabilistic Models to Predict the Number Density of Molecular Clouds in Astronomy
Denoising Diffusion Probabilistic Models (DDPMs) have become the mainstream generative
approach in the Machine Learning and Computer Vision area, achieving state-of-the-art …
approach in the Machine Learning and Computer Vision area, achieving state-of-the-art …