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Found 8 papers, 3 papers with code
Statistical Inference for Coadded Astronomical Images
Coadded astronomical images are created by stacking multiple single-exposure images.
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS).
Machine Learning and Cosmology
Methods based on machine learning have recently made substantial inroads in many corners of cosmology.
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN).
SHAPing the Gas: Understanding Gas Shapes in Dark Matter Haloes with Interpretable Machine Learning
In this study we explore a machine learning approach for modelling the dependence of gas shapes on dark matter and baryonic properties.
Cosmology and Nongalactic Astrophysics
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks
In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet.
