1.9k citations · 19k across the 159 of their papers we have counts for
160 papers
Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
J. Gonzalez, P. Holloway, T. Collett +70
We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learnin…
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…
WavPool: A New Block for Deep Neural Networks
Samuel D. McDermott, M. Voetberg, Brian Nord
Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavele…
The Dark Energy Survey Six-Year Calibration Star Catalog
E. S. Rykoff, D. L. Tucker, D. L. Burke +64
This Technical Note presents a catalog of calibrated reference stars that was generated by the Forward Calibration Method (FGCM) pipeline (arXiv:1706.01542) as part of the FGCM pho…
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
A. Ćiprijanović, A. Lewis, K. Pedro +4
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to t…
Neural Inference of Gaussian Processes for Time Series Data of Quasars
Egor Danilov, Aleksandra Ćiprijanović, Brian Nord
The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is…