Zooniverse

Abstract:

Through the lens of the LSST Science Collaborations’ experience, we advocate for new, improved ways to fund large, complex collaborations as they work in preparation for and on peta-scale surveys. We advocate for the establishment of programs to support research and infrastructure that enables innovative collaborative research on such scales.

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.

Abstract:

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Abstract:

Abstract Recent observations and simulations have revealed the dominance of secular processes over mergers in driving the growth of both supermassive black holes (SMBH) and galaxy evolution. Here we obtain narrowband imaging of AGN powered outflows in a sample of 12 galaxies with disk-dominated morphologies, whose history is assumed to be merger-free. We detect outflows in 10/12 sources in narrow band imaging of the $\mathrm{\left[ O \small {III}\right] }$ $5007~\mathring{\rm A}$ emission using filters on the Shane-3m telescope. We calculate a mean outflow rate for these AGN of $0.95\pm 0.14~\rm {M}_{\odot }~\rm {yr}^{-1}$. This exceeds the mean accretion rate of their SMBHs ($0.054\pm 0.039~\rm {M}_{\odot }~\rm {yr}^{-1}$) by a factor of ∼18. Assuming that the galaxy must provide at least enough material to power both the AGN and the outflow, this gives a lower limit on the average inflow rate of $\sim 1.01\pm 0.14~\rm {M}_{\odot }~\rm {yr}^{-1}$, a rate which simulations show can be achieved by bars, spiral arms and cold accretion. We compare our disk dominated sample to a sample of nearby AGN with merger dominated histories and show that the black hole accretion rates in our sample are 5 times higher (4.2σ) and the outflow rates are 5 times lower ( 2.6σ). We suggest that this could be a result of the geometry of the smooth, planar inflow in a secular dominated system, which is both spinning up the black hole to increase accretion efficiency and less affected by feedback from the outflow, than in a merger-driven system with chaotic quasi-spherical inflows. This work provides further evidence that secular processes are sufficient to fuel SMBH growth.