Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Understanding how rotationally supported discs transform into dispersion-dominated spheroids is central to our comprehension of galaxy evolution. Morphological transformation is largely merger-driven. While major mergers can efficiently create spheroids, recent work has highlighted the significant role of other processes, like minor mergers, in driving morphological change. Given their rich merger histories, spheroids typically exhibit large fractions of ‘ex situ’ stellar mass, i.e. mass that is accreted, via mergers, from external objects. This is particularly true for the most massive galaxies, whose stellar masses typically cannot be attained without a large number of mergers. Here, we explore an unusual population of extremely massive (M_* > 10¹¹M_⊙) spheroids, in the Horizon-AGN simulation, which exhibit anomalously low ex situ mass fractions, indicating that they form without recourse to significant merging. These systems form in a single minor-merger event (with typical merger mass ratios of 0.11–0.33), with a specific orbital configuration, where the satellite orbit is virtually co-planar with the disc of the massive galaxy. The merger triggers a catastrophic change in morphology, over only a few hundred Myr, coupled with strong in situ star formation. While this channel produces a minority (∼5 per cent) of such galaxies, our study demonstrates that the formation of at least some of the most massive spheroids need not involve major mergers – or any significant merging at all – contrary to what is classically believed.

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.