Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images
Publications of the Astronomical Society of the Pacific IOP Publishing 131:1004 (2019) 108011
Abstract:
This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a self-organizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data.Comparing Galaxy Clustering in Horizon-AGN Simulated Lightcone Mocks and VIDEO Observations
(2019)
Developing a unified pipeline for large-scale structure data analysis with angular power spectra -- I. The importance of redshift-space distortions for galaxy number counts
Monthly Notices of the Royal Astronomical Society, Volume 489, Issue 3, November 2019, Pages 3385–3402
Abstract:
We develop a cosmological parameter estimation code for (tomographic) angular power spectra analyses of galaxy number counts, for which we include, for the first time, redshift-space distortions (RSDs) in the Limber approximation. This allows for a speed-up in computation time, and we emphasize that only angular scales where the Limber approximation is valid are included in our analysis. Our main result shows that a correct modelling of RSD is crucial not to bias cosmological parameter estimation. This happens not only for spectroscopy-detected galaxies, but even in the case of galaxy surveys with photometric redshift estimates. Moreover, a correct implementation of RSD is especially valuable in alleviating the degeneracy between the amplitude of the underlying matter power spectrum and the galaxy bias. We argue that our findings are particularly relevant for present and planned observational campaigns, such as the Euclid satellite or the Square Kilometre Array, which aim at studying the cosmic large-scale structure and trace its growth over a wide range of redshifts and scales.
Author Correction: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project.
Scientific data (2019)