A ghost in the toast: TESS background light produces a false “transit” across τ Ceti
Research Notes of the AAS American Astronomical Society 3:10 (2019) 145
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.Author Correction: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project.
Scientific data (2019)
Abstract:
An amendment to this paper has been published and can be accessed via a link at the top of the paper.Secularly powered outflows from AGN: the dominance of non-merger driven supermassive black hole growth
Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) (2019)
Abstract:
The Frequency of Dust Lanes in Edge-on Spiral Galaxies Identified by Galaxy Zoo in KiDS Imaging of GAMA Targets
ASTRONOMICAL JOURNAL 158:3 (2019) ARTN 103